| Internet-Draft | DAP-PPM | August 2023 |
| Geoghegan, et al. | Expires 3 March 2024 | [Page] |
There are many situations in which it is desirable to take measurements of data which people consider sensitive. In these cases, the entity taking the measurement is usually not interested in people's individual responses but rather in aggregated data. Conventional methods require collecting individual responses and then aggregating them, thus representing a threat to user privacy and rendering many such measurements difficult and impractical. This document describes a multi-party distributed aggregation protocol (DAP) for privacy preserving measurement (PPM) which can be used to collect aggregate data without revealing any individual user's data.¶
This note is to be removed before publishing as an RFC.¶
The latest revision of this draft can be found at https://ietf-wg-ppm.github.io/draft-ietf-ppm-dap/draft-ietf-ppm-dap.html. Status information for this document may be found at https://datatracker.ietf.org/doc/draft-ietf-ppm-dap/.¶
Discussion of this document takes place on the Privacy Preserving Measurement Working Group mailing list (mailto:ppm@ietf.org), which is archived at https://mailarchive.ietf.org/arch/browse/ppm/. Subscribe at https://www.ietf.org/mailman/listinfo/ppm/.¶
Source for this draft and an issue tracker can be found at https://github.com/ietf-wg-ppm/draft-ietf-ppm-dap.¶
This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79.¶
Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet-Drafts is at https://datatracker.ietf.org/drafts/current/.¶
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This Internet-Draft will expire on 3 March 2024.¶
Copyright (c) 2023 IETF Trust and the persons identified as the document authors. All rights reserved.¶
This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (https://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Revised BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Revised BSD License.¶
This document describes the Distributed Aggregation Protocol (DAP) for privacy preserving measurement. The protocol is executed by a large set of clients and a small set of servers. The servers' goal is to compute some aggregate statistic over the clients' inputs without learning the inputs themselves. This is made possible by distributing the computation among the servers in such a way that, as long as at least one of them executes the protocol honestly, no input is ever seen in the clear by any server.¶
(*) Indicates a change that breaks wire compatibility with the previous draft.¶
05:¶
04:¶
03:¶
02:¶
DAP-Auth-Token header; this is now
optional.)¶
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here.¶
The output of the aggregation function computed over a batch of measurements and an aggregation parameter. As defined in [VDAF].¶
A share of the aggregate result emitted by an Aggregator. Aggregate shares are reassembled by the Collector into the aggregate result, which is the final output of the aggregation function. As defined in [VDAF].¶
The function computed over the Clients' measurements. As defined in [VDAF].¶
Parameter used to prepare a set of measurements for aggregation (e.g., the candidate prefixes for Poplar1 from Section 8 of [VDAF]). As defined in [VDAF].¶
An endpoint that receives input shares from Clients and validates and aggregates them with the help of the other Aggregators.¶
A set of reports (i.e., measurements) that are aggregated into an aggregate result.¶
The time difference between the oldest and newest report in a batch.¶
A parameter of a query issued by the Collector that specifies the time range of the reports in the batch.¶
A party that uploads a report.¶
The endpoint that selects the aggregation parameter and receives the aggregate result.¶
The Aggregator that executes the aggregation and collection sub-protocols as instructed by the Leader.¶
An Aggregator's share of a measurement. The input shares are output by the VDAF sharding algorithm. As defined in [VDAF].¶
An Aggregator's share of the refined measurement resulting from successful execution of the VDAF preparation phase. Many output shares are combined into an aggregate share during the VDAF aggregation phase. As defined in [VDAF].¶
The Aggregator that coordinates aggregation and collection with the Helper.¶
A plaintext input emitted by a Client (e.g., a count, summand, or string), before any encryption or secret sharing is applied. Depending on the VDAF in use, multiple values may be grouped into a single measurement. As defined in [VDAF].¶
The minimum number of reports in a batch.¶
The output of the VDAF sharding algorithm broadcast to each of the Aggregators. As defined in [VDAF].¶
A cryptographically protected measurement uploaded to the Leader by a Client. Comprised of a set of report shares.¶
An encrypted input share comprising a piece of a report.¶
This document uses the presentation language of [RFC8446] to define messages in the DAP protocol. Encoding and decoding of these messages as byte strings also follows [RFC8446].¶
The protocol is executed by a large set of Clients and a pair of servers
referred to as "Aggregators". Each Client's input to the protocol is its
measurement (or set of measurements, e.g., counts of some user behavior). Given
the input set of measurements x_1, ..., x_n held by n Clients, and an
aggregation parameter p shared by the Aggregators, the goal of DAP is to
compute y = F(p, x_1, ..., x_n) for some function F while revealing nothing
else about the measurements. We call F the "aggregation function."¶
This protocol is extensible and allows for the addition of new cryptographic schemes that implement the VDAF interface specified in [VDAF]. Candidates include:¶
VDAFs rely on secret sharing to protect the privacy of the measurements. Rather than sending its input in the clear, each Client shards its measurement into a pair of "input shares" and sends an input share to each of the Aggregators. This provides two important properties:¶
The overall system architecture is shown in Figure 1.¶
+--------+
| |
| Client +----+
| | |
+--------+ |
|
+--------+ | +------------+ +-----------+
| | +-----> | | |
| Client +----------> Leader <---------> Collector |
| | +-----> | | |
+--------+ | +-----^------+ +-----------+
| |
+--------+ | |
| | | |
| Client +----+ +-----V------+
| | | |
+--------+ | Helper |
| |
+------------+
The main participants in the protocol are as follows:¶
The entity which wants to obtain the aggregate of the measurements generated by the Clients. Any given measurement task will have a single Collector.¶
The endpoints which directly take the measurement(s) and report them to the DAP protocol. In order to provide reasonable levels of privacy, there must be a large number of Clients.¶
An endpoint which receives report shares. Each Aggregator works with its co-Aggregator to compute the aggregate result. Any given measurement task will have two Aggregators: a Leader and a Helper.¶
The Aggregator responsible for coordinating the protocol. It receives the reports, splits them into report shares, distributes the report shares to the Helper, and orchestrates the process of computing the aggregate result as requested by the Collector.¶
The Aggregator assisting the Leader with the computation. The protocol is designed so that the Helper is relatively lightweight, with most of the operational burdern born by the Leader.¶
The basic unit of DAP is the "task" which represents a single measurement process (though potentially aggregating multiple batches of measurements). The definition of a task includes the following parameters:¶
These parameters are distributed to the Clients, Aggregators, and Collector before the task begins. This document does not specify a distribution mechanism, but it is important that all protocol participants agree on the task's configuration. Each task is identified by a unique 32-byte ID which is used to refer to it in protocol messages.¶
During the lifetime of a task, each Client records its own measurement value(s), packages them up into a report, and sends them to the Leader. Each share is separately encrypted for each Aggregator so that even though they pass through the Leader, the Leader is unable to see or modify them. Depending on the task, the Client may only send one report or may send many reports over time.¶
The Leader distributes the shares to the Helper and orchestrates the process of verifying them (see Section 2.2) and assembling them into a final aggregate result for the Collector. Depending on the VDAF, it may be possible to incrementally process each report as it comes in, or may be necessary to wait until the entire batch of reports is received.¶
An essential task of any data collection pipeline is ensuring that the data being aggregated is "valid". In DAP, input validation is complicated by the fact that none of the entities other than the Client ever sees that Client's plaintext measurement.¶
In order to address this problem, the Aggregators engage in a secure, multi-party computation specified by the chosen VDAF [VDAF] in order to prepare a report for aggregation. At the beginning of this computation, each Aggregator is in possession of an input share uploaded by the Client. At the end of the computation, each Aggregator is in possession of either an "output share" that is ready to be aggregated or an indication that a valid output share could not be computed.¶
To facilitate this computation, the input shares generated by the Client include information used by the Aggregators during aggregation in order to validate their corresponding output shares. For example, Prio3 includes a zero-knowledge proof of the input's validity (see Section 7.1 of [VDAF]). which the Aggregators can jointly verify and reject the report if it cannot be verified. However, they do not learn anything about the individual report other than that it is valid.¶
The specific properties attested to in the proof vary depending on the
measurement being taken. For instance, to measure the time the user took
performing a given task the proof might demonstrate that the value reported was
within a certain range (e.g., 0-60 seconds). By contrast, to report which of a
set of N options the user select, the report might contain N integers and
the proof would demonstrate that N-1 were 0 and the other was 1.¶
It is important to recognize that "validity" is distinct from "correctness". For instance, the user might have spent 30s on a task but the Client might report 60s. This is a problem with any measurement system and DAP does not attempt to address it; it merely ensures that the data is within acceptable limits, so the Client could not report 10^6s or -20s.¶
Communications between DAP participants are carried over HTTPS [RFC9110]. HTTPS provides server authentication and confidentiality. Use of HTTPS is REQUIRED.¶
DAP is made up of several sub-protocols in which different subsets of the protocol's participants interact with each other.¶
In those cases where a channel between two participants is tunneled through another protocol participant, DAP mandates the use of public-key encryption using [HPKE] to ensure that only the intended recipient can see a message in the clear.¶
In other cases, DAP requires HTTPS client authentication as well as server authentication. Any authentication scheme that is composable with HTTP is allowed. For example:¶
DAP-Auth-Token HTTP header described in
[I-D.draft-dcook-ppm-dap-interop-test-design-04].¶
This flexibility allows organizations deploying DAP to use existing well-known HTTP authentication mechanisms that they already support. Discovering what authentication mechanisms are supported by a DAP participant is outside of this document's scope.¶
Errors can be reported in DAP both at the HTTP layer and within challenge objects as defined in Section 8. DAP servers can return responses with an HTTP error response code (4XX or 5XX). For example, if the Client submits a request using a method not allowed in this document, then the server MAY return HTTP status code 405 Method Not Allowed.¶
When the server responds with an error status, it SHOULD provide additional information using a problem document [RFC7807]. To facilitate automatic response to errors, this document defines the following standard tokens for use in the "type" field (within the DAP URN namespace "urn:ietf:params:ppm:dap:error:"):¶
| Type | Description |
|---|---|
| invalidMessage | A message received by a protocol participant could not be parsed or otherwise was invalid. |
| unrecognizedTask | An endpoint received a message with an unknown task ID. |
| unrecognizedAggregationJob | An endpoint received a message with an unknown aggregation job ID. |
| outdatedConfig | The message was generated using an outdated configuration. |
| reportRejected | Report could not be processed for an unspecified reason. |
| reportTooEarly | Report could not be processed because its timestamp is too far in the future. |
| batchInvalid | The batch boundary check for Collector's query failed. |
| invalidBatchSize | There are an invalid number of reports in the batch. |
| batchQueriedTooManyTimes | The maximum number of batch queries has been exceeded for one or more reports included in the batch. |
| batchMismatch | Aggregators disagree on the report shares that were aggregated in a batch. |
| unauthorizedRequest | Authentication of an HTTP request failed (see Section 3.1). |
| missingTaskID | HPKE configuration was requested without specifying a task ID. |
| stepMismatch | The Aggregators disagree on the current step of the DAP aggregation protocol. |
| batchOverlap | A request's query includes reports that were previously collected in a different batch. |
This list is not exhaustive. The server MAY return errors set to a URI other than those defined above. Servers MUST NOT use the DAP URN namespace for errors not listed in the appropriate IANA registry (see Section 8.4). The "detail" member of the Problem Details document includes additional diagnostic information.¶
When the task ID is known (see Section 4.2), the problem document SHOULD include an additional "taskid" member containing the ID encoded in Base 64 using the URL and filename safe alphabet with no padding defined in Sections 5 and 3.2 of [RFC4648].¶
In the remainder of this document, the tokens in the table above are used to refer to error types, rather than the full URNs. For example, an "error of type 'invalidMessage'" refers to an error document with "type" value "urn:ietf:params:ppm:dap:error:invalidMessage".¶
This document uses the verbs "abort" and "alert with [some error message]" to describe how protocol participants react to various error conditions. This implies HTTP status code 400 Bad Request unless explicitly specified otherwise.¶
DAP has three major interactions which need to be defined:¶
Each of these interactions is defined in terms of "resources". In this section we define these resources and the messages used to act on them.¶
The following are some basic type definitions used in other messages:¶
/* ASCII encoded URL. e.g., "https://example.com" */
opaque Url<1..2^16-1>;
uint64 Duration; /* Number of seconds elapsed between two instants */
uint64 Time; /* seconds elapsed since start of UNIX epoch */
/* An interval of time of length duration, where start is included and (start +
duration) is excluded. */
struct {
Time start;
Duration duration;
} Interval;
/* An ID used to uniquely identify a report in the context of a DAP task. */
opaque ReportID[16];
/* The various roles in the DAP protocol. */
enum {
collector(0),
client(1),
leader(2),
helper(3),
(255)
} Role;
/* Identifier for a server's HPKE configuration */
uint8 HpkeConfigId;
/* An HPKE ciphertext. */
struct {
HpkeConfigId config_id; /* config ID */
opaque enc<1..2^16-1>; /* encapsulated HPKE key */
opaque payload<1..2^32-1>; /* ciphertext */
} HpkeCiphertext;
/* Represent a zero-length byte string. */
struct {} Empty;
¶
DAP uses the 16-byte ReportID as the nonce parameter for the VDAF
measurement_to_input_shares and prep_init methods (see [VDAF], Section 5). Thus for a VDAF to be compatible with DAP, it MUST specify a NONCE_SIZE
of 16 bytes.¶
Aggregated results are computed based on sets of reports, called "batches". The Collector influences which reports are used in a batch via a "query." The Aggregators use this query to carry out the aggregation flow and produce aggregate shares encrypted to the Collector.¶
This document defines the following query types:¶
enum {
reserved(0), /* Reserved for testing purposes */
time_interval(1),
fixed_size(2),
(255)
} QueryType;
¶
The time_interval query type is described in Section 4.1.1; the fixed_size query type is described in Section 4.1.2. Future specifications may introduce new query types as needed (see Section 8.2). A query includes parameters used by the Aggregators to select a batch of reports specific to the given query type. A query is defined as follows:¶
opaque BatchID[32];
enum {
by_batch_id(0),
current_batch(1),
} FixedSizeQueryType;
struct {
FixedSizeQueryType query_type;
select (FixedSizeQuery.query_type) {
by_batch_id: BatchID batch_id;
current_batch: Empty;
}
} FixedSizeQuery;
struct {
QueryType query_type;
select (Query.query_type) {
case time_interval: Interval batch_interval;
case fixed_size: FixedSizeQuery fixed_size_query;
}
} Query;
¶
The parameters pertaining to each query type are described in one of the subsections below. The query is issued in-band as part of the collect sub-protocol (Section 4.6). Its content is determined by the "query type", which in turn is encoded by the "query configuration" configured out-of-band. All query types have the following configuration parameters in common:¶
min_batch_size - The smallest number of reports the batch is allowed to
include. In a sense, this parameter controls the degree of privacy that will
be obtained: the larger the minimum batch size, the higher degree of privacy.
However, this ultimately depends on the application and the nature of the
measurements and aggregation function.¶
time_precision - Clients use this value to truncate their report timestamps;
see Section 4.4. Additional semantics may apply, depending on the query
type. (See Section 4.6.5 for details.)¶
The parameters pertaining to specific query types are described in the relevant subsection below.¶
The first query type, time_interval, is designed to support applications in
which reports are collected over a long period of time. The Collector specifies
a "batch interval" that determines the time range for reports included in the
batch. For each report in the batch, the time at which that report was generated
(see Section 4.4) MUST fall within the batch interval specified by the
Collector.¶
Typically the Collector issues queries for which the batch intervals are
continuous, monotonically increasing, and have the same duration. For example,
the sequence of batch intervals (1659544000, 1000), (1659545000, 1000),
(1659546000, 1000), (1659547000, 1000) satisfies these conditions. (The
first element of the pair denotes the start of the batch interval and the second
denotes the duration.) Of course, there are cases in which Collector may need to
issue queries out-of-order. For example, a previous batch might need to be
queried again with a different aggregation parameter (e.g, for Poplar1). In
addition, the Collector may need to vary the duration to adjust to changing
report upload rates.¶
The fixed_size query type is used to support applications in which the
Collector needs the ability to strictly control the sample size. This is
particularly important for controlling the amount of noise added to reports by
Clients (or added to aggregate shares by Aggregators) in order to achieve
differential privacy.¶
For this query type, the Aggregators group reports into arbitrary batches such that each batch has roughly the same number of reports. These batches are identified by opaque "batch IDs", allocated in an arbitrary fashion by the Leader.¶
To get the aggregate of a batch, the Collector issues a query specifying the
batch ID of interest (see Section 4.1). The Collector may not know which batch ID
it is interested in; in this case, it can also issue a query of type
current_batch, which allows the Leader to select a recent batch to aggregate.
The Leader SHOULD select a batch which has not yet began collection.¶
In addition to the minimum batch size common to all query types, the
configuration includes a parameter max_batch_size that determines maximum
number of reports per batch.¶
Implementation note: The goal for the Aggregators is to aggregate precisely
min_batch_size reports per batch. Doing so, however, may be challenging for
Leader deployments in which multiple, independent nodes running the aggregate
sub-protocol (see Section 4.5) need to be coordinated. The maximum batch
size is intended to allow room for error. Typically the difference between the
minimum and maximum batch size will be a small fraction of the target batch size
for each batch.¶
[OPEN ISSUE: It may be feasible to require a fixed batch size, i.e.,
min_batch_size == max_batch_size. We should know better once we've had some
implementation/deployment experience.]¶
Prior to the start of execution of the protocol, each participant must agree on the configuration for each task. A task is uniquely identified by its task ID:¶
opaque TaskID[32];¶
The task ID value MUST be a globally unique sequence of bytes. Each task has the following parameters associated with it:¶
leader_aggregator_endpoint: A URL relative to which the Leader's API
endpoints can be found.¶
helper_aggregator_endpoint: A URL relative to which the Helper's API
endpoints can be found.¶
max_batch_query_count: The maximum number of times a batch of reports may be
queried by the Collector.¶
task_expiration: The time up to which Clients are expected to upload to this
task. The task is considered completed after this time. Aggregators MAY reject
reports that have timestamps later than task_expiration.¶
In addition, in order to facilitate the aggregation and collect protocols, each of the Aggregators is configured with following parameters:¶
collector_hpke_config: The [HPKE] configuration of the Collector
(described in Section 4.4.1); see Section 6 for information about the
HPKE configuration algorithms.¶
vdaf_verify_key: The VDAF verification key shared by the Aggregators. This
key is used in the aggregation sub-protocol (Section 4.5). The security
requirements are described in Section 7.4.1.¶
Finally, the Collector is configured with the HPKE secret key corresponding to
collector_hpke_config.¶
A task's parameters are immutable for the lifetime of that task. The only way to change parameters or to rotate secret values like collector HPKE configuration or the VDAF verification key is to configure a new task.¶
DAP is defined in terms of "resources", such as reports (Section 4.4), aggregation jobs (Section 4.5), and collection jobs (Section 4.6). Each resource has an associated URI. Resource URIs are specified by a sequence of string literals and variables. Variables are expanded into strings according to the following rules:¶
{leader} and {helper} are replaced with the base URL of the
Leader and Helper respectively (the base URL is defined in
Section 4.2).¶
{task-id}, {aggregation-job-id}, and {collection-job-id} are
replaced with the task ID (Section 4.2), aggregation job ID
(Section 4.5.1), and collection job ID (Section 4.6.1) respectively. The
value MUST be encoded in its URL-safe, unpadded Base 64 representation as
specified in Sections 5 and 3.2 of [RFC4648].¶
For example, resource URI {leader}/tasks/{task-id}/reports might be expanded
into
https://example.com/tasks/8BY0RzZMzxvA46_8ymhzycOB9krN-QIGYvg_RsByGec/reports¶
Clients periodically upload reports to the Leader. Each report contains two "report shares", one for the Leader and another for the Helper. The Helper's report share is transmitted by the Leader during the aggregation sub-protocol (see Section 4.5).¶
Before the Client can upload its report to the Leader, it must know the HPKE configuration of each Aggregator. See Section 6 for information on HPKE algorithm choices.¶
Clients retrieve the HPKE configuration from each Aggregator by sending an HTTP
GET request to {aggregator}/hpke_config. Clients MAY specify a query parameter
task_id whose value is the task ID whose HPKE configuration they want. If the
Aggregator does not recognize the task ID, then it MUST abort with error
unrecognizedTask.¶
An Aggregator is free to use different HPKE configurations for each task with
which it is configured. If the task ID is missing from the Client's request,
the Aggregator MAY abort with an error of type missingTaskID, in which case
the Client SHOULD retry the request with a well-formed task ID included.¶
An Aggregator responds to well-formed requests with HTTP status code 200 OK and
an HpkeConfigList value, with media type "application/dap-hpke-config-list".
The HpkeConfigList structure contains one or more HpkeConfig structures in
decreasing order of preference. This allows an Aggregator to support multiple
HPKE configurations simultaneously.¶
[TODO: Allow Aggregators to return HTTP status code 403 Forbidden in deployments that use authentication to avoid leaking information about which tasks exist.]¶
HpkeConfig HpkeConfigList<1..2^16-1>;
struct {
HpkeConfigId id;
HpkeKemId kem_id;
HpkeKdfId kdf_id;
HpkeAeadId aead_id;
HpkePublicKey public_key;
} HpkeConfig;
opaque HpkePublicKey<1..2^16-1>;
uint16 HpkeAeadId; /* Defined in [HPKE] */
uint16 HpkeKemId; /* Defined in [HPKE] */
uint16 HpkeKdfId; /* Defined in [HPKE] */
¶
[OPEN ISSUE: Decide whether to expand the width of the id.]¶
Aggregators MUST allocate distinct id values for each HpkeConfig in an
HpkeConfigList.¶
The Client MUST abort if any of the following happen for any HPKE config request:¶
Aggregators SHOULD use HTTP caching to permit client-side caching of this resource [RFC5861]. Aggregators SHOULD favor long cache lifetimes to avoid frequent cache revalidation, e.g., on the order of days. Aggregators can control this cached lifetime with the Cache-Control header, as follows:¶
Cache-Control: max-age=86400¶
Clients SHOULD follow the usual HTTP caching [RFC9111] semantics for HPKE configurations.¶
Note: Long cache lifetimes may result in Clients using stale HPKE configurations; Aggregators SHOULD continue to accept reports with old keys for at least twice the cache lifetime in order to avoid rejecting reports.¶
Clients upload reports by using an HTTP PUT to
{leader}/tasks/{task-id}/reports. The payload is a Report, with media type
"application/dap-report", structured as follows:¶
struct {
ReportID report_id;
Time time;
} ReportMetadata;
struct {
ReportMetadata report_metadata;
opaque public_share<0..2^32-1>;
HpkeCiphertext leader_encrypted_input_share;
HpkeCiphertext helper_encrypted_input_share;
} Report;
¶
report_metadata is public metadata describing the report.¶
report_id is used by the Aggregators to ensure the report appears in at
most one batch (see Section 4.5.1.4). The Client MUST generate
this by generating 16 random bytes using a cryptographically secure random
number generator.¶
time is the time at which the report was generated. The Client SHOULD
round this value down to the nearest multiple of the task's
time_precision in order to ensure that that the timestamp cannot be used
to link a report back to the Client that generated it.¶
public_share is the public share output by the VDAF sharding algorithm. Note
that the public share might be empty, depending on the VDAF.¶
leader_encrypted_input_share is the Leader's encrypted input share.¶
helper_encrypted_input_share is the Helper's encrypted input share.¶
Aggregators MAY require clients to authenticate when uploading reports (see Section 7.2.1). If it is used, Client authentication MUST use a scheme that meets the requirements in Section 3.1.¶
To generate a report, the Client begins by sharding its measurement into input shares and the public share using the VDAF's sharding algorithm (Section 5.1 of [VDAF]), using the report ID as the nonce:¶
(public_share, input_shares) = Vdaf.measurement_to_input_shares(
measurement, /* plaintext measurement */
report_id, /* nonce */
rand, /* randomness for sharding algorithm */
)
¶
The last input comprises the randomness consumed by the sharding algorithm. The sharding randomness is a random byte string of length specified by the VDAF. The Client MUST generate this using a cryptographically secure random number generator.¶
The Client then wraps each input share in the following structure:¶
struct {
Extension extensions<0..2^16-1>;
opaque payload<0..2^32-1>;
} PlaintextInputShare;
¶
Field extensions is set to the list of extensions intended to be consumed by
the given Aggregator. (See Section 4.4.3.) Field payload is set to the
Aggregator's input share output by the VDAF sharding algorithm.¶
Next, the Client encrypts each PlaintextInputShare plaintext_input_share as
follows:¶
enc, payload = SealBase(pk, "dap-05 input share" || 0x01 || server_role, input_share_aad, plaintext_input_share)¶
where pk is the Aggregator's public key; server_role is the Role of the
intended recipient (0x02 for the Leader and 0x03 for the Helper),
plaintext_input_share is the Aggregator's PlaintextInputShare, and
input_share_aad is an encoded message of type InputShareAad defined below,
constructed from the same values as the corresponding fields in the report. The
SealBase() function is as specified in [HPKE], Section 6.1 for the
ciphersuite indicated by the HPKE configuration.¶
struct {
TaskID task_id;
ReportMetadata report_metadata;
opaque public_share<0..2^32-1>;
} InputShareAad;
¶
The Leader responds to well-formed requests with HTTP status code 201 Created. Malformed requests are handled as described in Section 3.2. Clients SHOULD NOT upload the same measurement value in more than one report if the Leader responds with HTTP status code 201 Created.¶
If the Leader does not recognize the task ID, then it MUST abort with error
unrecognizedTask.¶
The Leader responds to requests whose Leader encrypted input share uses an
out-of-date or unknown HpkeConfig.id value, indicated by
HpkeCiphertext.config_id, with error of type 'outdatedConfig'. When the Client
receives an 'outdatedConfig' error, it SHOULD invalidate any cached
HpkeConfigList and retry with a freshly generated Report. If this retried upload
does not succeed, the Client SHOULD abort and discontinue retrying.¶
If a report's ID matches that of a previously uploaded report, the Leader MUST
ignore it. In addition, it MAY alert the Client with error reportRejected. See
the implementation note in Section 4.5.1.4.¶
The Leader MUST ignore any report pertaining to a batch that has already been
collected (see Section 4.5.1.4 for details). Otherwise, comparing
the aggregate result to the previous aggregate result may result in a privacy
violation. Note that this is also enforced by the Helper during the aggregation
sub-protocol. The Leader MAY also abort the upload protocol and alert the
Client with error reportRejected.¶
The Leader MAY ignore any report whose timestamp is past the task's
task_expiration. When it does so, it SHOULD also abort the upload protocol and
alert the Client with error reportRejected. Client MAY choose to opt out of
the task if its own clock has passed task_expiration.¶
The Leader may need to buffer reports while waiting to aggregate them (e.g.,
while waiting for an aggregation parameter from the Collector; see
Section 4.6). The Leader SHOULD NOT accept reports whose timestamps are too
far in the future. Implementors MAY provide for some small leeway, usually no
more than a few minutes, to account for clock skew. If the Leader rejects a
report for this reason, it SHOULD abort the upload protocol and alert the Client
with error reportTooEarly. In this situation, the Client MAY re-upload the
report later on.¶
If the Leader's input share contains an unrecognized extension, or if two extensions have the same ExtensionType, then the Leader MAY abort the upload request with error "invalidMessage". Note that this behavior is not mandatory because it requires the Leader to decrypt its input share.¶
Each PlaintextInputShare carries a list of extensions that Clients use to convey additional information to the Aggregator. Some extensions might be intended for both Aggregators; others may only be intended for a specific Aggregator. (For example, a DAP deployment might use some out-of-band mechanism for an Aggregator to verify that reports come from authenticated Clients. It will likely be useful to bind the extension to the input share via HPKE encryption.)¶
Each extension is a tag-length encoded value of the following form:¶
struct {
ExtensionType extension_type;
opaque extension_data<0..2^16-1>;
} Extension;
enum {
TBD(0),
(65535)
} ExtensionType;
¶
Field "extension_type" indicates the type of extension, and "extension_data" contains information specific to the extension.¶
Extensions are mandatory-to-implement: If an Aggregator receives a report containing an extension it does not recognize, then it MUST reject the report. (See Section 4.5.1.4 for details.)¶
Once a set of Clients have uploaded their reports to the Leader, the Leader can begin the process of validating and aggregating them with the Helper. To enable the system to handle large batches of reports, this process can be parallelized across many "aggregation jobs" in which small subsets of the reports are processed independently. Each aggregation job is associated with exactly one DAP task, but a task can have many aggregation jobs.¶
The primary objective of an aggregation job is to run the VDAF preparation process described in [VDAF], Section 5.2 for each report in the job. Preparation has two purposes:¶
In general, refinement and verification are not distinct computations, since for some VDAFs, verification may only be achieved implicitly as a result of the refinement process. We instead think of these as properties of the output shares themselves: if preparation succeeds, then the resulting output shares are guaranteed to combine into a valid, refined measurement.¶
VDAF preparation is mapped onto an aggregation job as illustrated in Figure 2. The protocol is comprised of a sequence of HTTP requests from the Leader to the Helper, the first of which includes the aggregation parameter, the Helper's report share for each report in the job, and for each report the initialization step for preparation. The Helper's response, along with each subsequent request and response, carries the remaining messages exchanged during preparation.¶
report, agg_param | v +--------+ +--------+ | Leader | | Helper | +--------+ +--------+ | AggregationJobInitReq: | | agg_param, prep_init | |---------------------------------------------------->| | AggregationJobResp: | | prep_resp(continue) | |<----------------------------------------------------| | AggregationJobContinueReq: | | prep_continue | |---------------------------------------------------->| | AggregationJobResp: | | prep_resp(continue) | |<----------------------------------------------------| | | ... ... | | | AggregationJobContinueReq: | | prep_continue | |---------------------------------------------------->| | AggregationJobResp: | | prep_resp(continue|finished) | |<----------------------------------------------------| | | v v leader_out_share helper_out_share
The number of steps, and the type of the responses, depends on the VDAF. The message structures and processing rules are specified in the following subsections.¶
In general, reports cannot be aggregated until the Collector specifies an aggregation parameter. However, in some situations it is possible to begin aggregation as soon as reports arrive. For example, Prio3 has just one valid aggregation parameter (the empty string). And there are use cases for Poplar1 in which aggregation can begin immediately (i.e., those for which the candidate prefixes/strings are fixed in advance).¶
An aggregation job can be thought of as having three phases:¶
These phases are described in the following subsections.¶
The Leader begins an aggregation job by choosing a set of candidate reports that pertain to the same DAP task and a job ID which MUST be unique within the scope of the task. The job ID is a 16-byte value, structured as follows:¶
opaque AggregationJobID[16];¶
The Leader can run this process for many sets of candidate reports in parallel as needed. After choosing a set of candidates, the Leader begins aggregation by splitting each report into report shares, one for each Aggregator. The Leader and Helper then run the aggregate initialization flow to accomplish two tasks:¶
The Leader and Helper initialization behavior is detailed below.¶
The Leader begins the aggregate initialization phase with the set of candidate reports as follows:¶
If any step invalidates the report, the Leader rejects the report and removes it from the set of candidate reports.¶
Next, for each report the Leader executes the following procedure:¶
(state, outbound) = Vdaf.ping_pong_leader_init(
vdaf_verify_key,
agg_param,
report_id,
public_share,
plaintext_input_share.payload)
¶
where:¶
vdaf_verify_key is the VDAF verification key for the task¶
agg_param is the VDAF aggregation parameter provided by the Collector (see
Section 4.6)¶
report_id is the report ID, used as the nonce for VDAF sharding¶
public_share is the report's public share¶
plaintext_input_share is the Leader's PlaintextInputShare¶
The methods are defined in Section 5.8 of [VDAF]. This process determines
the initial per-report state, as well as the initial outbound message to
send to the Helper. If state is of type Rejected, then the report is
rejected and removed from the set of candidate reports, and no message is sent
to the Helper.¶
If state is of type Continued, then the Leader constructs a PrepareInit
message structured as follows:¶
struct {
ReportMetadata report_metadata;
opaque public_share<0..2^32-1>;
HpkeCiphertext encrypted_input_share;
} ReportShare;
struct {
ReportShare report_share;
opaque payload<0..2^32-1>;
} PrepareInit;
¶
Each of these messages is constructed as follows:¶
report_share.report_metadata is the report's metadata.¶
report_share.public_share is the report's public share.¶
report_share.encrypted_input_share is the intended recipient's (i.e.
Helper's) encrypted input share.¶
payload is set to the outbound message computed by the previous step.¶
It is not possible for state to be of type Finished during Leader
initialization.¶
Once all the report shares have been initialized, the Leader creates an
AggregationJobInitReq message structured as follows:¶
struct {
QueryType query_type;
select (PartialBatchSelector.query_type) {
case time_interval: Empty;
case fixed_size: BatchID batch_id;
};
} PartialBatchSelector;
struct {
opaque agg_param<0..2^32-1>;
PartialBatchSelector part_batch_selector;
PrepareInit prepare_inits<1..2^32-1>;
} AggregationJobInitReq;
¶
[[OPEN ISSUE: Consider sending report shares separately (in parallel) to the aggregate instructions. Right now, aggregation parameters and the corresponding report shares are sent at the same time, but this may not be strictly necessary.]]¶
This message consists of:¶
agg_param: The VDAF aggregation parameter.¶
part_batch_selector: The "partial batch selector" used by the Aggregators to
determine how to aggregate each report:¶
For fixed_size tasks, the Leader specifies a "batch ID" that determines
the batch to which each report for this aggregation job belongs.¶
[OPEN ISSUE: For fixed_size tasks, the Leader is in complete control over which batch a report is included in. For time_interval tasks, the Client has some control, since the timestamp determines which batch window it falls in. Is this desirable from a privacy perspective? If not, it might be simpler to drop the timestamp altogether and have the agg init request specify the batch window instead.]¶
The indicated query type MUST match the task's query type. Otherwise, the
Helper MUST abort with error invalidMessage.¶
This field is called the "partial" batch selector because, depending on the
query type, it may not determine a batch. In particular, if the query type is
time_interval, the batch is not determined until the Collector's query is
issued (see Section 4.1).¶
prepare_inits: the sequence of PrepareInit messages constructed in the
previous step.¶
Finally, the Leader sends a PUT request to
{helper}/tasks/{task-id}/aggregation_jobs/{aggregation-job-id}. The payload
is set to AggregationJobInitReq and the media type is set to
"application/dap-aggregation-job-init-req".¶
The Leader MUST authenticate its requests to the Helper using a scheme that meets the requirements in Section 3.1.¶
The Helper's response will be an AggregationJobResp message (see
Section 4.5.1.2. The response's prepare_resps must include exactly
the same report IDs in the same order as the Leader's AggregationJobInitReq.
Otherwise, the Leader MUST abort the aggregation job.¶
[[OPEN ISSUE: consider relaxing this ordering constraint. See issue#217.]]¶
Otherwise, the Leader proceeds as follows with each report:¶
If the inbound prep response has type "continue", then the Leader computes¶
(state, outbound) = Vdaf.ping_pong_leader_continued(agg_param,
prev_state,
inbound)
¶
where:¶
agg_param is the VDAF aggregation parameter provided by the Collector (see
Section 4.6)¶
prev_state is the state computed earlier by calling
Vdaf.ping_pong_leader_init or Vdaf.ping_pong_leader_continued¶
inbound is the message payload in the PrepareResp¶
If outbound != None, then the Leader stores state and outbound and
proceeds to Section 4.5.2.1. If outbound == None, then
the preparation process is complete: either state == Rejected(), in which
case the Leader rejects the report and removes it from the candidate set; or
state == Finished(out_share), in which case preparation is complete and the
Leader stores the output share for use in the collection sub-protocol
Section 4.6.¶
report_too_early, in
which case the Leader MAY include the report in a subsequent aggregation job.¶
(Note: Since VDAF preparation completes in a constant number of rounds, it will never be the case that some reports are completed and others are not.)¶
The Helper begins an aggregation job when it receives an AggregationJobInitReq
message from the Leader. For each PrepareInit conveyed by this message, the
Helper attempts to initialize VDAF preparation (see Section 5.1 of [VDAF])
just as the Leader does. If successful, it includes the result in its response
that the Leader will use to continue preparing the report.¶
To begin this process, the Helper checks if it recognizes the task ID. If not,
then it MUST abort with error unrecognizedTask.¶
Next, the Helper checks that the report IDs in
AggregationJobInitReq.prepare_inits are all distinct. If two preparation
initialization messages have the same report ID, then the Helper MUST abort with
error invalidMessage.¶
The Helper is now ready to process each report share into an outbound prepare step to return to the server. The responses will be structured as follows:¶
enum {
continue(0),
finished(1)
reject(2),
(255)
} PrepareRespState;
enum {
batch_collected(0),
report_replayed(1),
report_dropped(2),
hpke_unknown_config_id(3),
hpke_decrypt_error(4),
vdaf_prep_error(5),
batch_saturated(6),
task_expired(7),
invalid_message(8),
report_too_early(9),
(255)
} PrepareError;
struct {
ReportID report_id;
PrepareRespState prepare_resp_state;
select (PrepareResp.prepare_resp_state) {
case continue: opaque payload<0..2^32-1>;
case finished: Empty;
case reject: PrepareError prepare_error;
};
} PrepareResp;
¶
First the Helper preprocesses each report as follows:¶
For any report that was rejected, the Helper sets the outbound preparation response to¶
struct {
ReportID report_id;
PrepareRespState prepare_resp_state = reject;
PrepareError prepare_error;
} PrepareResp;
¶
where report_id is the report ID and prepare_error is the indicated error.
For all other reports it initializes the VDAF prep state as follows (let
inbound denote the payload of the prep step sent by the Leader):¶
(state, outbound) = Vdaf.ping_pong_helper_init(vdaf_verify_key,
agg_param,
report_id,
public_share,
plaintext_input_share.payload)
¶
where:¶
vdaf_verify_key is the VDAF verification key for the task¶
agg_param is the VDAF aggregation parameter sent in the
AggregationJobInitReq¶
report_id is the report ID¶
public_share is the report's public share¶
plaintext_input_share is the Helper's PlaintextInputShare¶
This procedure determines the initial per-report state, as well as the
initial outbound to send in response to the Leader. If state is of type
Rejected, then the Helper responds with¶
struct {
ReportID report_id;
PrepareRespState prepare_resp_state = reject;
PrepareError prepare_error = vdaf_prep_error;
} PrepareResp;
¶
Otherwise the Helper responds with¶
struct {
ReportID report_id;
PrepareRespState prepare_resp_state = continue;
opaque payload<0..2^32-1> = outbound;
} PrepareResp;
¶
Finally, the Helper creates an AggregationJobResp to send to the Leader. This
message is structured as follows:¶
struct {
PrepareResp prepare_resps<1..2^32-1>;
} AggregationJobResp;
¶
where prepare_resps are the outbound prep steps computed in the previous step.
The order MUST match AggregationJobInitReq.prepare_inits.¶
The Helper responds to the Leader with HTTP status code 201 Created and a body
consisting of the AggregationJobResp, with media type
"application/dap-aggregation-job-resp".¶
Changing an aggregation job's parameters is illegal, so further requests to
PUT /tasks/{tasks}/aggregation_jobs/{aggregation-job-id} for the same
aggregation-job-id but with a different AggregationJobInitReq in the body
MUST fail with an HTTP client error status code.¶
Additionally, it is not possible to rewind or reset the state of an aggregation job. Once an aggregation job has been continued at least once (see Section 4.5.2), further requests to initialize that aggregation job MUST fail with an HTTP client error status code.¶
Finally, if state == Continued(prep_state), then the Helper stores state to
prepare for the next continuation step (Section 4.5.2.2).
Otherwise, if state == Finished(out_share), then the Helper stores out_share
for use in the collection sub-protocol (Section 4.6).¶
In the continuation phase, the Leader drives the VDAF preparation of each report in the candidate report set until the underlying VDAF moves into a terminal state, yielding an output share for both Aggregators or a rejection.¶
Whether this phase is reached depends on the VDAF: for 1-round VDAFs, like Prio3, processing has already completed. Continuation is required for VDAFs that require more than one round.¶
The Leader begins each step of aggregation continuation with a prep state object
state and an outbound message outbound for each report in the candidate set.¶
The Leader advances its aggregation job to the next step (step 1 if this is the first continuation after initialization). Then it instructs the Helper to advance the aggregation job to the step the Leader has just reached. For each report the Leader constructs a preparation continuation message:¶
struct {
ReportID report_id;
opaque payload<0..2^32-1>;
} PrepareContinue;
¶
where report_id is the report ID associated with state and outbound, and
payload is set to the outbound message.¶
Next, the Leader sends a POST request to the aggregation job URI used during initialization (see Section 4.5.1.1) with media type "application/dap-aggregation-job-continue-req" and body structured as:¶
struct {
uint16 step;
PrepareContinue prepare_continues<1..2^32-1>;
} AggregationJobContinueReq;
¶
The step field is the step of DAP aggregation that the Leader just reached and
wants the Helper to advance to. The prepare_continues field is the sequence of
preparation continuation messages constructed in the previous step. The
PrepareContinues MUST be in the same order as the previous aggregate request.¶
The Leader MUST authenticate its requests to the Helper using a scheme that meets the requirements in Section 3.1.¶
The Helper's response will be an AggregationJobResp message (see
Section 4.5.1.2). The response's prepare_resps must include
exactly the same report IDs in the same order as the Leader's
AggregationJobContinueReq. Otherwise, the Leader MUST abort the aggregation
job.¶
[[OPEN ISSUE: consider relaxing this ordering constraint. See issue#217.]]¶
Otherwise, the Leader proceeds as follows with each report:¶
If the inbound prep response type is "continue" and the state is
Continued(prep_state), then the Leader computes¶
(state, outbound) = Vdaf.ping_pong_leader_continued(agg_param,
state,
inbound)
¶
where inbound is the message payload. If outbound != None, then the
Leader stores state and outbound and proceeds to another continuation
step. If outbound == None, then the preparation process is complete: either
state == Rejected(), in which case the Leader rejects the report and
removes it from the candidate set; or state == Finished(out_share), in
which case preparation is complete and the Leader stores the output share for
use in the collection sub-protocol Section 4.6.¶
state == Finished(out_share), then
preparation is complete and the Leader stores the output share for use in
the collection flow (Section 4.6).¶
The Helper begins each step of continuation with a sequence of state objects,
which will be Continued(prep_state), one for each report in the candidate set.¶
The Helper awaits an HTTP POST request to the aggregation job URI from the
Leader, the body of which is an AggregationJobContinueReq as specified in
Section 4.5.2.1.¶
Next, it checks that it recognizes the task ID. If not, then it MUST abort with
error unrecognizedTask.¶
Next, it checks if it recognizes the indicated aggregation job ID. If not, it
MUST abort with error unrecognizedAggregationJob.¶
Next, the Helper checks that:¶
state objects¶
AggregationJobContinueReq.step is not equal to 0¶
If any of these checks fail, then the Helper MUST abort with error
invalidMessage. Additionally, if any prep step appears out of order relative
to the previous request, then the Helper MAY abort with error invalidMessage.
(Note that a report may be missing, in which case the Helper should assume the
Leader rejected it.)¶
[OPEN ISSUE: Issue 438: It may be useful for the Leader to explicitly signal rejection.]¶
Next, the Helper checks if the continuation step indicated by the request is
correct. (For the first AggregationJobContinueReq the value should be 1;
for the second the value should be 2; and so on.) If the Leader is one step
behind (e.g., the Leader has resent the previous HTTP request), then the Helper
MAY attempt to recover by re-sending the previous AggregationJobResp. In this
case it SHOULD verify that the contents of the AggregationJobContinueReq are
identical to the previous message (see Section 4.5.2.3).
Otherwise, if the step is incorrect, the Helper MUST abort with error
stepMismatch.¶
The Helper is now ready to continue preparation for each report. Let inbound
denote the payload of the prep step. The Helper computes the following:¶
(state, outbound) = Vdaf.ping_pong_helper_continued(agg_param,
state,
inbound)
¶
If state == Rejected(), then the Helper's response is¶
struct {
ReportID report_id;
PrepareRespState prepare_resp_state = reject;
PrepareError prepare_error = vdaf_prep_error;
} PrepareResp;
¶
Otherwise, if outbound != None, then the Helper's response is¶
struct {
ReportID report_id;
PrepareRespState prepare_resp_state = continue;
opaque payload<0..2^32-1> = outbound;
} PrepareResp;
¶
Otherwise, if outbound == None, then the Helper's resposne is¶
struct {
ReportID report_id;
PrepareRespState prepare_resp_state = finished;
} PrepareResp;
¶
Next, the Helper constructs an AggregationJobResp message
(Section 4.5.1.2) with each prep step. The order of the prep steps
MUST match the Leader's request. It responds to the Leader with HTTP status 200
OK, media type application/dap-aggregation-job-resp, and a body consisting of
the AggregationJobResp.¶
Finally, if state == Continued(prep_state), then the Helper stores state to
prepare for the next continuation step (Section 4.5.2.2).
Otherwise, if state == Finished(out_share), then the Helper stores out_share
for use in the collection sub-protocol (Section 4.6).¶
AggregationJobContinueReq messages contain a step field, allowing
Aggregators to ensure that their peer is on an expected step of the DAP
aggregation protocol. In particular, the intent is to allow recovery from a
scenario where the Helper successfully advances from step n to n+1, but its
AggregationJobResp response to the Leader gets dropped due to something like a
transient network failure. The Leader could then resend the request to have the
Helper advance to step n+1 and the Helper should be able to retransmit the
AggregationJobContinueReq that was previously dropped. To make that kind of
recovery possible, Aggregator implementations SHOULD checkpoint the most recent
step's prep state and messages to durable storage such that the Leader can
re-construct continuation requests and the Helper can re-construct continuation
responses as needed.¶
When implementing an aggregation step skew recovery strategy, the Helper SHOULD
ensure that the Leader's AggregationJobContinueReq message did not change when
it was re-sent (i.e., the two messages must be identical). This prevents the
Leader from re-winding an aggregation job and re-running an aggregation step
with different parameters.¶
[[OPEN ISSUE: Allowing the Leader to "rewind" aggregation job state of the Helper may allow an attack on privacy. For instance, if the VDAF verification key changes, the prep shares in the Helper's response would change even if the consistency check is made. Security analysis is required. See #401.]]¶
One way the Helper could address this would be to store a digest of the Leader's request, indexed by aggregation job ID and step, and refuse to service a request for a given aggregation step unless it matches the previously seen request (if any).¶
In this phase, the Collector requests aggregate shares from each Aggregator and then locally combines them to yield a single aggregate result. In particular, the Collector issues a query to the Leader (Section 4.1), which the Aggregators use to select a batch of reports to aggregate. Each Aggregator emits an aggregate share encrypted to the Collector so that it can decrypt and combine them to yield the aggregate result. This entire process is composed of two interactions:¶
Once complete, the Collector computes the final aggregate result as specified in Section 4.6.3.¶
This overall process is referred to as a "collection job".¶
First, the Collector chooses a collection job ID:¶
opaque CollectionJobID[16];¶
This ID value MUST be unique within the scope of the corresponding DAP task.¶
To initiate the collection job, the collector issues a PUT request to
{leader}/tasks/{task-id}/collection_jobs/{collection-job-id}. The body of the
request has media type "application/dap-collect-req", and it is structured as
follows:¶
struct {
Query query;
opaque agg_param<0..2^32-1>; /* VDAF aggregation parameter */
} CollectionReq;
¶
The named parameters are:¶
query, the Collector's query. The indicated query type MUST match the task's
query type. Otherwise, the Leader MUST abort with error "invalidMessage".¶
agg_param, an aggregation parameter for the VDAF being executed. This is the
same value as in AggregationJobInitReq (see Section 4.5.1.1).¶
Collectors MUST authenticate their requests to Leaders using a scheme that meets the requirements in Section 3.1.¶
Depending on the VDAF scheme and how the Leader is configured, the Leader and Helper may already have prepared a sufficient number of reports satisfying the query and be ready to return the aggregate shares right away. However, this is not always the case. In fact, for some VDAFs, it is not be possible to begin running aggregation jobs (Section 4.5) until the Collector initiates a collection job. This is because, in general, the aggregation parameter is not known until this point. In certain situations it is possible to predict the aggregation parameter in advance. For example, for Prio3 the only valid aggregation parameter is the empty string. For these reasons, the collection job is handled asynchronously.¶
Upon receipt of a CollectionReq, the Leader begins by checking that it
recognizes the task ID in the request path. If not, it MUST abort with error
unrecognizedTask.¶
The Leader MAY further validate the request according to the requirements in Section 4.6.5 and abort with the indicated error, though some conditions such as the number of valid reports may not be verifiable while handling the CollectionReq message, and the batch will have to be re-validated later on regardless.¶
If the Leader finds the CollectionReq to be valid, it immediately responds with HTTP status 201.¶
The Leader then begins working with the Helper to aggregate the reports satisfying the query (or continues this process, depending on the VDAF) as described in Section 4.5.¶
Changing a collection job's parameters is illegal, so further requests to
PUT /tasks/{tasks}/collection_jobs/{collection-job-id} for the same
collection-job-id but with a different CollectionReq in the body MUST fail
with an HTTP client error status code.¶
After receiving the response to its CollectionReq, the Collector makes an HTTP
POST request to the collection job URI to check on the status of the collect
job and eventually obtain the result. If the collection job is not finished
yet, the Leader responds with HTTP status 202 Accepted. The response MAY include
a Retry-After header field to suggest a polling interval to the Collector.¶
Asynchronously from any request from the Collector, the Leader attempts to run the collection job. It first checks whether it can construct a batch for the collection job by applying the requirements in Section 4.6.5. If so, then the Leader obtains the Helper's aggregate share following the aggregate-share request flow described in Section 4.6.2. If not, it either aborts the collection job or tries again later, depending on which requirement in Section 4.6.5 was not met.¶
Once both aggregate shares are successfully obtained, the Leader responds to
subsequent HTTP POST requests to the collection job with HTTP status code 200 OK
and a body consisting of a Collection:¶
struct {
PartialBatchSelector part_batch_selector;
uint64 report_count;
Interval interval;
HpkeCiphertext leader_encrypted_agg_share;
HpkeCiphertext helper_encrypted_agg_share;
} Collection;
¶
The body's media type is "application/dap-collection". The Collection
structure includes the following:¶
part_batch_selector: Information used to bind the aggregate result to the
query. For fixed_size tasks, this includes the batch ID assigned to the batch
by the Leader. The indicated query type MUST match the task's query type.¶
[OPEN ISSUE: What should the Collector do if the query type doesn't match?]¶
report_count: The number of reports included in the batch.¶
interval: The smallest interval of time that contains the timestamps of all
reports included in the batch, such that the interval's start and duration are
both multiples of the task's time_precision parameter. Note that in the case
of a time_interval type query (see Section 4.1), this interval can be smaller
than the one in the corresponding CollectionReq.query.¶
leader_encrypted_agg_share: The Leader's aggregate share, encrypted to the
Collector.¶
helper_encrypted_agg_share: The Helper's aggregate share, encrypted to the
Collector.¶
If obtaining aggregate shares fails, then the Leader responds to subsequent HTTP POST requests to the collection job with an HTTP error status and a problem document as described in Section 3.2.¶
The Leader MAY respond with HTTP status 204 No Content to requests to a collection job if the results have been deleted.¶
The Collector can send an HTTP DELETE request to the collection job, which indicates to the Leader that it can abandon the collection job and discard all state related to it.¶
The reason a POST is used to poll the state of a collection job instead of a GET is because of the fixed-size query mode (see Section 4.1.2). Collectors may make a query against the current batch, and it is the Leader's responsibility to keep track of what batch is current for some task. Polling a collection job is the only point at which it is safe for the Leader to change its set of current batches, since it constitutes acknowledgement on the Collector's part that it received the response to some previous PUT request to the collection jobs resource.¶
This means that polling a collection job can have the side effect of changing the set of current batches in the Leader, and thus using a GET is inappropriate.¶
The Leader must obtain the Helper's encrypted aggregate share before it can complete a collection job. To do this, the Leader first computes a checksum over the reports included in the batch. The checksum is computed by taking the SHA256 [SHS] hash of each report ID from the Client reports included in the aggregation, then combining the hash values with a bitwise-XOR operation.¶
Then the Leader sends a POST request to
{helper}/tasks/{task-id}/aggregate_shares with the following message:¶
struct {
QueryType query_type;
select (BatchSelector.query_type) {
case time_interval: Interval batch_interval;
case fixed_size: BatchID batch_id;
};
} BatchSelector;
struct {
BatchSelector batch_selector;
opaque agg_param<0..2^32-1>;
uint64 report_count;
opaque checksum[32];
} AggregateShareReq;
¶
The media type of the request is "application/dap-aggregate-share-req". The message contains the following parameters:¶
batch_selector: The "batch selector", which encodes parameters used to
determine the batch being aggregated. The value depends on the query type for
the task:¶
The indicated query type MUST match the task's query type. Otherwise, the Helper MUST abort with "invalidMessage".¶
agg_param: The opaque aggregation parameter for the VDAF being executed.
This value MUST match the AggregationJobInitReq message for each aggregation
job used to compute the aggregate shares (see Section 4.5.1.1) and the
aggregation parameter indicated by the Collector in the CollectionReq message
(see Section 4.6.1).¶
report_count: The number number of reports included in the batch.¶
checksum: The batch checksum.¶
Leaders MUST authenticate their requests to Helpers using a scheme that meets the requirements in Section 3.1.¶
To handle the Leader's request, the Helper first ensures that it recognizes the
task ID in the request path. If not, it MUST abort with error
unrecognizedTask. The Helper then verifies that the request meets the
requirements for batch parameters following the procedure in
Section 4.6.5.¶
Next, it computes a checksum based on the reports that satisfy the query, and
checks that the report_count and checksum included in the request match its
computed values. If not, then it MUST abort with an error of type
"batchMismatch".¶
Next, it computes the aggregate share agg_share corresponding to the set of
output shares, denoted out_shares, for the batch interval, as follows:¶
agg_share = Vdaf.out_shares_to_agg_share(agg_param, out_shares)¶
Implementation note: For most VDAFs, it is possible to aggregate output shares as they arrive rather than wait until the batch is collected. To do so however, it is necessary to enforce the batch parameters as described in Section 4.6.5 so that the Aggregator knows which aggregate share to update.¶
The Helper then encrypts agg_share under the Collector's HPKE public key as
described in Section 4.6.4, yielding encrypted_agg_share.
Encryption prevents the Leader from learning the actual result, as it only has
its own aggregate share and cannot compute the Helper's.¶
The Helper responds to the Leader with HTTP status code 200 OK and a body
consisting of an AggregateShare, with media type
"application/dap-aggregate-share":¶
struct {
HpkeCiphertext encrypted_aggregate_share;
} AggregateShare;
¶
encrypted_aggregate_share.config_id is set to the Collector's HPKE config ID.
encrypted_aggregate_share.enc is set to the encapsulated HPKE context enc
computed above and encrypted_aggregate_share.ciphertext is the ciphertext
encrypted_agg_share computed above.¶
The Helper's handling of this request MUST be idempotent. That is, if multiple
identical, valid AggregateShareReqs are received, they should all yield the
same response while only consuming one unit of the task's
max_batch_query_count (see Section 4.6.5).¶
After receiving the Helper's response, the Leader uses the HpkeCiphertext to finalize a collection job (see Section 4.6.3).¶
Once an AggregateShareReq has been issued for the batch determined by a given query, it is an error for the Leader to issue any more aggregation jobs for additional reports that satisfy the query. These reports will be rejected by the Helper as described in Section 4.5.1.4.¶
Before completing the collection job, the Leader also computes its own aggregate
share agg_share by aggregating all of the prepared output shares that fall
within the batch interval. Finally, it encrypts its aggregate share under the
Collector's HPKE public key as described in Section 4.6.4.¶
Once the Collector has received a collection job from the Leader, it can decrypt
the aggregate shares and produce an aggregate result. The Collector decrypts
each aggregate share as described in Section 4.6.4. Once the
Collector successfully decrypts all aggregate shares, it unshards the aggregate
shares into an aggregate result using the VDAF's agg_shares_to_result
algorithm. In particular, let leader_agg_share denote the Leader's aggregate
share, helper_agg_share denote the Helper's aggregate share, let
report_count denote the report count sent by the Leader, and let agg_param
be the opaque aggregation parameter. The final aggregate result is computed as
follows:¶
agg_result = Vdaf.agg_shares_to_result(agg_param,
[leader_agg_share, helper_agg_share],
report_count)
¶
Before a Leader runs a collection job or a Helper responds to an AggregateShareReq, it must first check that the job or request does not violate the parameters associated with the DAP task. It does so as described here. Where we say that an Aggregator MUST abort with some error, then:¶
First the Aggregator checks that the batch respects any "boundaries" determined by the query type. These are described in the subsections below. If the boundary check fails, then the Aggregator MUST abort with an error of type "batchInvalid".¶
Next, the Aggregator checks that batch contains a valid number of reports, as determined by the query type. If the size check fails, then Helpers MUST abort with an error of type "invalidBatchSize". Leaders SHOULD wait for more reports to be validated and try the collection job again later.¶
Next, the Aggregator checks that the batch has not been queried too many times.
This is determined by the maximum number of times a batch can be queried,
max_batch_query_count. If the batch has been queried with more than
max_batch_query_count distinct aggregation parameters, the Aggregator MUST
abort with error of type "batchQueriedTooManyTimes".¶
Finally, the Aggregator checks that the batch does not contain a report that was included in any previous batch. If this batch overlap check fails, then the Aggregator MUST abort with error of type "batchOverlap". For time_interval tasks, it is sufficient (but not necessary) to check that the batch interval does not overlap with the batch interval of any previous query. If this batch interval check fails, then the Aggregator MAY abort with error of type "batchOverlap".¶
[[OPEN ISSUE: #195 tracks how we might relax this constraint to allow for more collect query flexibility. As of now, this is quite rigid and doesn't give the Collector much room for mistakes.]]¶
The batch boundaries are determined by the time_precision field of the query
configuration. For the batch_interval included with the query, the Aggregator
checks that:¶
batch_interval.duration >= time_precision (this field determines,
effectively, the minimum batch duration)¶
batch_interval.start and batch_interval.duration are divisible by
time_precision¶
These measures ensure that Aggregators can efficiently "pre-aggregate" output shares recovered during the aggregation sub-protocol.¶
The query configuration specifies the minimum batch size, min_batch_size. The
Aggregator checks that len(X) >= min_batch_size, where X is the set of
reports successfully aggregated into the batch.¶
For fixed_size tasks, the batch boundaries are defined by opaque batch IDs. Thus the Aggregator needs to check that the query is associated with a known batch ID:¶
by_batch_id, the Leader
checks that the provided batch ID corresponds to a batch ID it returned in a
previous collection for the task.¶
AggregationJobInitReq
for the task.¶
The query configuration specifies the minimum batch size, min_batch_size, and
maximum batch size, max_batch_size. The Aggregator checks that len(X) >=
min_batch_size and len(X) <= max_batch_size, where X is the set of reports
successfully aggregated into the batch.¶
The DAP protocol has inherent constraints derived from the tradeoff between privacy guarantees and computational complexity. These tradeoffs influence how applications may choose to utilize services implementing the specification.¶
The design in this document has different assumptions and requirements for different protocol participants, including Clients, Aggregators, and Collectors. This section describes these capabilities in more detail.¶
Clients have limited capabilities and requirements. Their only inputs to the protocol are (1) the parameters configured out of band and (2) a measurement. Clients are not expected to store any state across any upload flows, nor are they required to implement any sort of report upload retry mechanism. By design, the protocol in this document is robust against individual Client upload failures since the protocol output is an aggregate over all inputs.¶
Leaders and Helpers have different operational requirements. The design in this document assumes an operationally competent Leader, i.e., one that has no storage or computation limitations or constraints, but only a modestly provisioned Helper, i.e., one that has computation, bandwidth, and storage constraints. By design, Leaders must be at least as capable as Helpers, where Helpers are generally required to:¶
In addition, for each DAP task, the Helper is required to:¶
Beyond the minimal capabilities required of Helpers, Leaders are generally required to:¶
In addition, for each DAP task, the Leader is required to:¶
Collectors statefully interact with Aggregators to produce an aggregate output. Their input to the protocol is the task parameters, configured out of band, which include the corresponding batch window and size. For each collect invocation, Collectors are required to keep state from the start of the protocol to the end as needed to produce the final aggregate output.¶
Collectors must also maintain state for the lifetime of each task, which includes key material associated with the HPKE key configuration.¶
Privacy comes at the cost of computational complexity. While affine-aggregatable encodings (AFEs) can compute many useful statistics, they require more bandwidth and CPU cycles to account for finite-field arithmetic during input-validation. The increased work from verifying inputs decreases the throughput of the system or the inputs processed per unit time. Throughput is related to the verification circuit's complexity and the available compute-time to each Aggregator.¶
Applications that utilize proofs with a large number of multiplication gates or a high frequency of inputs may need to limit inputs into the system to meet bandwidth or compute constraints. Some methods of overcoming these limitations include choosing a better representation for the data or introducing sampling into the data collection methodology.¶
[[TODO: Discuss explicit key performance indicators, here or elsewhere.]]¶
A soft real-time system should produce a response within a deadline to be useful. This constraint may be relevant when the value of an aggregate decreases over time. A missed deadline can reduce an aggregate's utility but not necessarily cause failure in the system.¶
An example of a soft real-time constraint is the expectation that input data can be verified and aggregated in a period equal to data collection, given some computational budget. Meeting these deadlines will require efficient implementations of the input-validation protocol. Applications might batch requests or utilize more efficient serialization to improve throughput.¶
Some applications may be constrained by the time that it takes to reach a privacy threshold defined by a minimum number of reports. One possible solution is to increase the reporting period so more samples can be collected, balanced against the urgency of responding to a soft deadline.¶
Not all DAP tasks have the same operational requirements, so the protocol is designed to allow implementations to reduce operational costs in certain cases.¶
In general, the Aggregators are required to keep state for tasks and all valid
reports for as long as collect requests can be made for them. In particular,
Aggregators must store a batch as long as the batch has not been queried more
than max_batch_query_count times. However, it is not always necessary to store
the reports themselves. For schemes like Prio3 [VDAF] in which reports are
verified only once, each Aggregator only needs to store its aggregate share for
each possible batch interval, along with the number of times the aggregate share
was used in a batch. This is due to the requirement that the batch interval
respect the boundaries defined by the DAP parameters. (See
Section 4.6.5.)¶
However, Aggregators are also required to implement several per-report checks that require retaining a number of data artifacts. For example, to detect replay attacks, it is necessary for each Aggregator to retain the set of report IDs of reports that have been aggregated for the task so far. Depending on the task lifetime and report upload rate, this can result in high storage costs. To alleviate this burden, DAP allows Aggregators to drop this state as needed, so long as reports are dropped properly as described in Section 4.5.1.4. Aggregators SHOULD take steps to mitigate the risk of dropping reports (e.g., by evicting the oldest data first).¶
Furthermore, the Aggregators must store data related to a task as long as the
current time has not passed this task's task_expiration. Aggregator MAY delete
the task and all data pertaining to this task after task_expiration.
Implementors SHOULD provide for some leeway so the Collector can collect the
batch after some delay.¶
In the absence of an application or deployment-specific profile specifying otherwise, a compliant DAP application MUST implement the following HPKE cipher suite:¶
DAP assumes an active attacker that controls the network and has the ability to statically corrupt any number of Clients, Aggregators, and Collectors. That is, the attacker can learn the secret state of any party prior to the start of its attack. For example, it may coerce a Client into providing malicious input shares for aggregation or coerce an Aggregator into diverting from the protocol specified (e.g., by divulging its input shares to the attacker).¶
In the presence of this adversary, DAP aims to achieve the privacy and robustness security goals described in [VDAF]'s Security Considerations section. Even if DAP achieves those goals, there are still some threats it does not defend against:¶
In this section, we enumerate the actors participating in a Distributed Aggregation Protocol deployment, enumerate their assets (secrets that are either inherently valuable or which confer some capability that enables further attack on the system), the capabilities that a malicious or compromised actor has, and potential mitigations for attacks enabled by those capabilities.¶
This model assumes that all participants have previously agreed upon and exchanged all shared parameters over some unspecified secure channel.¶
Clients may affect the quality of aggregate results by reporting false measurements.¶
Clients may upload reports to a task multiple times. The VDAF will prove that each report is valid, but the results of a VDAF like Prio3Sum can be skewed if a Client submits many valid reports. Attackers may also attempt ballot stuffing attacks, trying to produce aggregations over batches containing nothing but synthetic reports with a known value and a single, legitimate report whose privacy is then compromised.¶
Aggregators may defeat the robustness of the system by emitting incorrect aggregate shares.¶
If Clients reveal identifying information to Aggregators (such as a trusted identity during Client authentication), Aggregators can learn which Clients are contributing reports.¶
Aggregators may attack robustness by selectively omitting reports from certain Clients.¶
Individual Aggregators may compromise availability of the system by refusing to emit aggregate shares.¶
Violate robustness. Any Aggregator can collude with a malicious Client to craft a proof that will fool honest Aggregators into accepting invalid measurements.¶
Aggregators (and the Collector) can count the total number of input shares, which could compromise user privacy (and differential privacy Section 7.5) if the presence or absence of a share for a given user is sensitive.¶
[[TODO: link to the Shan et al. I-D on differential privacy in DAP once it is published.]]¶
The Leader is also an Aggregator, and so all the assets, capabilities and mitigations available to Aggregators also apply to the Leader.¶
Shrinking the anonymity set. The Leader instructs the Helper to construct aggregate shares and so could request aggregations over dangerously few reports.¶
Relaying messages between Helper and Collector in the collect sub-protocol. These messages are not authenticated, meaning the leader can:¶
Send collect parameters to the Helper that do not reflect the parameters chosen by the Collector¶
BatchSelector and aggregation
parameter in the AAD used to encrypt aggregate shares.¶
[[OPEN ISSUE: Should we have authentication in either direction between the Helper and the Collector? #155]]¶
If all Aggregators collude (e.g. by promiscuously sharing unencrypted input shares), then none of the properties of the system hold. Accordingly, such scenarios are outside of the threat model.¶
We assume the existence of attackers on the network links between participants. Most passive network attacks are mitigated by DAP's requirement of HTTPS for all traffic and mutual authentication for key protocol interactions (see Section 3). Nonetheless, there remain information leaks that deployments should be aware of.¶
Attackers may observe messages exchanged between participants at the IP layer.¶
Tampering with network traffic. Attackers may drop messages or inject new messages into communications between participants.¶
[[OPEN ISSUE: The threat model for Prio --- as it's described in the original paper and [BBCGGI19] --- considers either a malicious Client (attacking robustness) or a malicious subset of Aggregators (attacking privacy). In particular, robustness isn't guaranteed if any one of the Aggregators is malicious; in theory it may be possible for a malicious Client and Aggregator to collude and break robustness. Is this a contingency we need to address? There are techniques in [BBCGGI19] that account for this; we need to figure out if they're practical.]]¶
Several attacks on privacy involve malicious clients uploading reports that are valid under the chosen VDAF but incorrect. For example, a DAP deployment might be measuring the heights of a human population and configure a VDAF to prove that measurements are values in the range of 80-250 cm. A malicious Client would not be able to claim a height of 400 cm, but they could submit multiple bogus reports inside the acceptable range, which would yield incorrect averages. More generally, DAP deployments are susceptible to Sybil attacks [Dou02].¶
In this type of attack, the adversary adds to a batch a number of reports that skew the aggregate result in its favor. For example, sending known measurements to the Aggregators can allow a Collector to shrink the effective anonymity set by subtracting the known measurements from the aggregate result. The result may reveal additional information about the honest measurements, leading to a privacy violation; or the result may have some property that is desirable to the adversary ("stats poisoning").¶
In settings where it is practical for each Client to have an identity provisioned (e.g., a user logged into a backend service or a hardware device programmed with an identity), Client authentication is a highly effective way for the Aggregators (or an authenticating proxy deployed between clients and the Aggregators; see Section 7.3) to ensure that all reports come from authentic Clients and to enforce policy on things like upload rates. Note that because the Helper never handles messages directly from the Clients, reports would have to use an extension (Section 4.4.3) to convey authentication information to the Helper.¶
However, in some deployments, it will not be practical to require Clients to authenticate, so Client authentication is not mandatory in DAP. For example, a widely distributed application that does not require its users to log in to any service has no obvious way to authenticate its report uploads.¶
Client reports can contain auxiliary information such as source IP, HTTP user agent or in deployments which use it, Client authentication information, which could be used by Aggregators to identify participating Clients or permit some attacks on robustness. This auxiliary information could be removed by having Clients submit reports to an anonymizing proxy server which would then use Oblivious HTTP [I-D.draft-ietf-ohai-ohttp-08] to forward reports to the DAP Leader, without requiring any server participating in DAP to be aware of whatever Client authentication or attestation scheme is in use.¶
Selection and distribution of DAP task parameters is out of band from DAP itself and thus not discussed in this document, but we must nonetheless discuss the security implications of some task parameter choices. Generally, attacks involving crafted DAP task parameters can be mitigated by having the the Aggregators refuse shared parameters that are trivially insecure (e.g., a minimum batch size of 1 report).¶
The verification key for a task SHOULD be chosen before any reports are generated. It SHOULD be fixed for the lifetime of the task and not be rotated. One way to ensure this is to include the verification key in a derivation of the task ID.¶
This consideration comes from current security analysis for existing VDAFs. For example, to ensure that the security proofs for Prio3 hold, the verification key MUST be chosen independently of the generated reports. This can be achieved as recommended above.¶
An important parameter of a DAP deployment is the minimum batch size. If a batch includes too few reports, then the aggregate result can reveal information about individual participants. Aggregators must enforce the agreed-upon minimum batch size during the collect protocol, but implementations may also opt out of participating in a DAP task if the minimum batch size is too small. This document does not specify how to choose minimum batch sizes.¶
The choice of VDAF can impact the computation required for a DAP Task. For instance, the Poplar1 VDAF [VDAF] when configured to compute a set of heavy hitters requires each measurement to be of the same bit-length which all parties need to agree on prior to VDAF execution. The computation required for such tasks can increase superlinearly as multiple rounds of evaluation are needed for each bit of the measurement value.¶
When dealing with variable length measurements (e.g domain names), it is necessary to pad them to convert into fixed-size measurements. When computing the heavy hitters from a batch of such measurements, we can early-abort the Poplar1 execution once we have reached the padding region for a candidate measurement. For smaller length measurements, this significantly reduces the cost of communication between Aggregators and the steps required for the computation. However, malicious Clients can still generate maximum length measurements forcing the system to always operate at worst-case performance.¶
[[TODO: Revisit this paragraph once https://github.com/cfrg/draft-irtf-cfrg-vdaf/issues/273 is resolved.]]¶
Therefore, care must be taken that a DAP deployment can comfortably handle computation of measurements for arbitrarily large sizes, otherwise, it may result in a DoS possibility for the entire system.¶
Optionally, DAP deployments can choose to ensure their aggregate results achieve differential privacy [Vad16]. A simple approach would require the Aggregators to add two-sided noise (e.g. sampled from a two-sided geometric distribution) to aggregate shares. Since each Aggregator is adding noise independently, privacy can be guaranteed even if all but one of the Aggregators is malicious. Differential privacy is a strong privacy definition, and protects users in extreme circumstances: even if an adversary has prior knowledge of every measurement in a batch except for one, that one record is still formally protected.¶
Most DAP protocols, including Prio and Poplar, are robust against malicious clients, but are not robust against malicious servers. Any Aggregator can simply emit bogus aggregate shares and undetectably spoil aggregates. If enough Aggregators were available, this could be mitigated by running the protocol multiple times with distinct subsets of Aggregators chosen so that no Aggregator appears in all subsets and checking all the aggregate results against each other. If all the protocol runs do not agree, then participants know that at least one Aggregator is defective, and it may be possible to identify the defector (i.e., if a majority of runs agree, and a single Aggregator appears in every run that disagrees). See #22 for discussion.¶
Prio deployments should ensure that Aggregators do not have common dependencies that would enable a single vendor to reassemble measurements. For example, if all participating Aggregators stored unencrypted input shares on the same cloud object storage service, then that cloud vendor would be able to reassemble all the input shares and defeat privacy.¶
This specification defines the following protocol messages, along with their corresponding media types types:¶
The definition for each media type is in the following subsections.¶
Protocol message format evolution is supported through the definition of new formats that are identified by new media types.¶
IANA [shall update / has updated] the "Media Types" registry at https://www.iana.org/assignments/media-types with the registration information in this section for all media types listed above.¶
[OPEN ISSUE: Solicit review of these allocations from domain experts.]¶
application¶
dap-hpke-config-list¶
N/A¶
None¶
only "8bit" or "binary" is permitted¶
see Section 4.2¶
N/A¶
this specification¶
N/A¶
N/A¶
see Authors' Addresses section¶
COMMON¶
N/A¶
see Authors' Addresses section¶
IESG¶
application¶
dap-report¶
N/A¶
None¶
only "8bit" or "binary" is permitted¶
see Section 4.4.2¶
N/A¶
this specification¶
N/A¶
N/A¶
see Authors' Addresses section¶
COMMON¶
N/A¶
see Authors' Addresses section¶
IESG¶
application¶
dap-aggregation-job-init-req¶
N/A¶
None¶
only "8bit" or "binary" is permitted¶
see Section 4.6¶
N/A¶
this specification¶
N/A¶
N/A¶
see Authors' Addresses section¶
COMMON¶
N/A¶
see Authors' Addresses section¶
IESG¶
application¶
dap-aggregation-job-resp¶
N/A¶
None¶
only "8bit" or "binary" is permitted¶
see Section 4.6¶
N/A¶
this specification¶
N/A¶
N/A¶
see Authors' Addresses section¶
COMMON¶
N/A¶
see Authors' Addresses section¶
IESG¶
application¶
dap-aggregation-job-continue-req¶
N/A¶
None¶
only "8bit" or "binary" is permitted¶
see Section 4.6¶
N/A¶
this specification¶
N/A¶
N/A¶
see Authors' Addresses section¶
COMMON¶
N/A¶
see Authors' Addresses section¶
IESG¶
application¶
dap-collect-req¶
N/A¶
None¶
only "8bit" or "binary" is permitted¶
see Section 4.6¶
N/A¶
this specification¶
N/A¶
N/A¶
see Authors' Addresses section¶
COMMON¶
N/A¶
see Authors' Addresses section¶
IESG¶
application¶
dap-collection¶
N/A¶
None¶
only "8bit" or "binary" is permitted¶
see Section 4.6¶
N/A¶
this specification¶
N/A¶
N/A¶
see Authors' Addresses section¶
COMMON¶
N/A¶
see Authors' Addresses section¶
IESG¶
This document requests creation of a new registry for Query Types. This registry should contain the following columns:¶
[TODO: define how we want to structure this registry when the time comes]¶
This document requests creation of a new registry for extensions to the Upload protocol. This registry should contain the following columns:¶
[TODO: define how we want to structure this registry when the time comes]¶
The following value [will be/has been] registered in the "IETF URN Sub-namespace for Registered Protocol Parameter Identifiers" registry, following the template in [RFC3553]:¶
Registry name: dap Specification: [[THIS DOCUMENT]] Repository: http://www.iana.org/assignments/dap Index value: No transformation needed.¶
Initial contents: The types and descriptions in the table in Section 3.2 above, with the Reference field set to point to this specification.¶