A Prio Instantiation for Vector Sums with an L1 Norm Bound on Contributions
draft-ietf-ppm-l1-bound-sum-00
| Document | Type | Active Internet-Draft (ppm WG) | |
|---|---|---|---|
| Authors | Martin Thomson , David Cook | ||
| Last updated | 2025-11-13 | ||
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draft-ietf-ppm-l1-bound-sum-00
Privacy Preserving Measurement M. Thomson
Internet-Draft Mozilla
Intended status: Standards Track D. Cook
Expires: 7 May 2026 ISRG
3 November 2025
A Prio Instantiation for Vector Sums with an L1 Norm Bound on
Contributions
draft-ietf-ppm-l1-bound-sum-00
Abstract
A Prio Verifiable Distributed Aggregation Function is defined that
supports vector or histogram addition, where the sum of the values in
the contribution is less than a chosen value.
About This Document
This note is to be removed before publishing as an RFC.
The latest revision of this draft can be found at
https://martinthomson.github.io/prio-l1-bound-sum/draft-thomson-ppm-
l1-bound-sum.html. Status information for this document may be found
at https://datatracker.ietf.org/doc/draft-ietf-ppm-l1-bound-sum/.
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This Internet-Draft will expire on 7 May 2026.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions and Definitions . . . . . . . . . . . . . . . . . 3
3. Prio3L1BoundSum Definition . . . . . . . . . . . . . . . . . 3
3.1. Chunk Length Selection . . . . . . . . . . . . . . . . . 4
3.2. Encoding and Decoding . . . . . . . . . . . . . . . . . . 4
3.3. Validity Circuit . . . . . . . . . . . . . . . . . . . . 5
4. Security Considerations . . . . . . . . . . . . . . . . . . . 6
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 7
6. References . . . . . . . . . . . . . . . . . . . . . . . . . 7
6.1. Normative References . . . . . . . . . . . . . . . . . . 7
6.2. Informative References . . . . . . . . . . . . . . . . . 7
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 7
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 7
1. Introduction
Existing Prio instantiations of a Verifiable Distributed Aggregation
Function (VDAF) [VDAF] all support a simple summation of
measurements. From Prio3Count (Section 7.4.1 of [VDAF]), which adds
measurements containing a single one or a zero value, to Prio3SumVec
(Section 7.4.3 of [VDAF]), which adds measurements containing a
vector where each dimension is a limited number of bits, all
instantations take the same basic form.
One case that is presently not included in the suite of
instantiations is the addition of vectors or histogram contributions,
where each measurement has an L1 bound. The L1 norm of a vector is
defined as the sum of its components. An L1 bound limits that sum to
some maximum.
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This document defines the Prio3L1BoundSum instantiation. This
instantiation limits the L1 norm of a vector or histogram to a value
that is one less than a chosen power of 2, or 2^n-1. This choice
significantly reduces the size of the encoding relative to a more
flexible limit.
This instantiation has similarities with other instantiations.
Unlike Prio3Histogram (Section 7.4.4 of [VDAF]), in which
measurements need to have an L1 norm of exactly 1, a valid
measurement for Prio3L1BoundSum can have an L1 norm equal to any
value between 0 and the chosen limit. Unlike Prio3MultiHotCountVec
(Section 7.4.5 of [VDAF]), in which each component can only be zero
or one, components in Prio3L1BoundSum can take any value up to the L1
bound as long as their sum is within that bound.
Section 3 defines the Prio3L1BoundSum VDAF.
2. Conventions and Definitions
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.
This document uses the terminology and functions defined in Section 2
of [VDAF].
3. Prio3L1BoundSum Definition
The Prio3L1BoundSum instantiation of Prio [CGB17] supports the
addition of a vector of integers.
The instantiation is summarized in Table 1.
+===========+===============================================+
| Parameter | Value |
+===========+===============================================+
| field | Field128 (Section 6.1.2 of [VDAF]) |
+-----------+-----------------------------------------------+
| Valid | L1BoundSum(field, length, bits, chunk_length) |
+-----------+-----------------------------------------------+
| PROOFS | 1 |
+-----------+-----------------------------------------------+
| XOF | XofTurboShake128 (Section 6.2.1 of [VDAF]) |
+-----------+-----------------------------------------------+
Table 1: Prio3L1BoundSum Parameters
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The function takes three parameters: length, bits, and chunk_length.
The vector contains "length" components, each of which is a non-
negative integer less than 2^bits.
3.1. Chunk Length Selection
The chunk_length parameter can be chosen in approximately the same
way as for Prio3SumVec, as detailed in Section 7.4.3.1 of [VDAF].
The difference is that Prio3L1BoundSum involves validation of bits *
(length + 1) values, which might increase the most efficient value
for chunk_length.
3.2. Encoding and Decoding
The encoded form of each measurement appends a bitwise decomposition
of the L1 norm (the sum of the vector components) to the encoding:
def encode(self, measurement: list[int]) -> list[F]:
encoded = []
weight = self.field(0)
for v in measurement:
weight += v
encoded += self.field.encode_into_bit_vec(v, self.bits)
w_bits = self.field.encode_into_bit_vec(weight, self.bits)
return encoded + w_bits
The encoded measurement has a total length of (length + 1) * bits.
This extra information is not included in the output share that is
submitted for aggregation. That is, the truncate() function emits
only the core measurements.
def truncate(self, meas: list[F]) -> list[F]:
return [
self.field.decode_from_bit_vec(m)
for m in chunks(meas, self.bits)
]
This uses a chunks(v, c) function that takes a list of values, v, and
a chunk length, c, to split v into multiple lists from v, where each
chunk has a length c.
The decode() function is therefore identical to that in Prio3SumVec.
def decode(self, output: list[F], _count) -> list[int]:
return [x.int() for x in output]
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3.3. Validity Circuit
The validity circuit for Prio3L1BoundSum uses an extended version of
the validity circuit used by Prio3SumVec, see Section 7.4.3 of
[VDAF].
The encoded measurement is checked to ensure that every component of
the vector – plus the added L1 norm – is encoded in the specified
number of bits. That is, the circuit checks that each component has
a value between 0 (inclusive) and 2^bits (exclusive) by checking that
each of the first "bits" bits of the value are either zero or one.
This process is identical to the Prio3SumVec check, except that one
additional value is checked.
The validity circuit then checks whether the added L1 norm value is
consistent with the encoded vector elements. The L1 norm is checked
by decoding the measurement values, including the encoded L1 norm,
recomputing the L1 norm as the sum of the individual components, and
subtracting the reported and computed values to confirm that they are
identical.
The complete circuit is specified in Figure 1.
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def eval(self, meas: list[F],
joint_rand: list[F], num_shares: int) -> list[F]:
assert len(meas) == (self.length + 1) * self.bits
shares_inv = self.field(num_shares).inv()
parallel_sum = ParallelSum(Mul(), chunk_length)
num_chunks = ceil(len(meas) / self.chunk_length)
pad_len = self.chunk_length * num_chunks - len(meas)
meas += [self.field(0)] * pad_len
range_check = self.field(0)
for (r, m) in zip(joint_rand, chunks(meas, self.chunk_length)):
inputs = []
for i in range(self.chunk_length):
inputs += [
r**(i + 1) * m[i],
m[i] - shares_inv,
]
range_check += parallel_sum.eval(self.field, inputs)
components = [
self.field.decode_from_bit_vec(m)
for m in chunks(meas, self.bits)
]
observed_weight = sum(components[:self.length])
claimed_weight = components[self.length]
weight_check = observed_weight - claimed_weight
return [range_check, weight_check]
Figure 1: Evaluation function for Prio3L1BoundSum
4. Security Considerations
The Prio3L1BoundSum VDAF is subject to the same considerations as
other Prio-based VDAFs. These considerations are detailed in
Section 9 of [VDAF].
In particular, this instantiation uses Field128 to ensure robustness
despite the use of joint randomness in proofs. Joint randomness
increases the risk of an attacker finding a combination of invalid
inputs that passes validation. A larger field increases the
computational cost of finding such a combination.
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5. IANA Considerations
This document registers a codepoint for Prio3L1BoundSum in the
"Verifiable Distributed Aggregation Functions (VDAF)" registry as
defined by Section 10 of [VDAF]. This entry contains the following
fields:
Value: 0x00000007
Scheme: Prio3L1BoundSum
Type: VDAF
Reference: RFCXXXX (this document)
6. References
6.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/rfc/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/rfc/rfc8174>.
[VDAF] Barnes, R., Cook, D., Patton, C., and P. Schoppmann,
"Verifiable Distributed Aggregation Functions", Work in
Progress, Internet-Draft, draft-irtf-cfrg-vdaf-15, 17 June
2025, <https://datatracker.ietf.org/doc/html/draft-irtf-
cfrg-vdaf-15>.
6.2. Informative References
[CGB17] Boneh, D. and H. Corrigan-Gibbs, "Prio: Private, Robust,
and Scalable Computation of Aggregate Statistics", USENIX
Symposium on Networked Systems Design and Implementation
(NSDI), 2017,
<https://dl.acm.org/doi/10.5555/3154630.3154652>.
Acknowledgments
Chris Patton provided extensive input into the construction of this
VDAF.
Authors' Addresses
Martin Thomson
Mozilla
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Email: mt@lowentropy.net
David Cook
ISRG
Email: divergentdave@gmail.com
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