



Privacy Preserving Measurement                                M. Thomson
Internet-Draft                                                   Mozilla
Intended status: Standards Track                                 D. Cook
Expires: 23 August 2026                                             ISRG
                                                        19 February 2026


     A Prio Instantiation for Vector Sums with an L1 Norm Bound on
                             Contributions
                     draft-ietf-ppm-l1-bound-sum-01

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://ietf-wg-
   ppm.github.io/draft-ietf-ppm-l1-bound-sum/draft-ietf-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/.

   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-l1-bound-sum.

Status of This Memo

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   provisions of BCP 78 and BCP 79.

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   material or to cite them other than as "work in progress."



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Copyright Notice

   Copyright (c) 2026 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
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   Please review these documents carefully, as they describe your rights
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   provided without warranty as described in the Revised BSD License.

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.  DAP Integration . . . . . . . . . . . . . . . . . . . . . . .   6
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .   7
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   7
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   7
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .   7
     7.2.  Informative References  . . . . . . . . . . . . . . . . .   8
   Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . .   8
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   8

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
   less than or equal to a predetermined maximum.

   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, notation conventions, 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.  It also uses bit_length(), which
   returns the minimum number of bits needed to encode an integer value.

   The instantiation is summarized in Table 1.

       +===========+==============================================+
       | Parameter | Value                                        |
       +===========+==============================================+
       | field     | Field128 (Section 6.1.2 of [VDAF])           |
       +-----------+----------------------------------------------+
       | Valid     | L1BoundSum(field, length, max, 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, max_value, and
   chunk_length.  The vector contains "length" components, each of which
   is a non-negative integer less than or equal to max_value.

   The value of max_value can be any positive integer.  A value of 1
   causes Prio3L1BoundSum to be nearly identical to Prio3Histogram,
   except that Prio3Histogram cannot encode an all-zero report.

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, where bits = max_value.bit_length().  This might
   increase the most efficient value for chunk_length relative to a
   similar encoding of Prio3SumVec.

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 = []
       erci = encode_range_checked_int
       for v in measurement:
           encoded += erci(self.field, v, self.max_value)
       weight = erci(self.field, sum(measurement), self.max_value)
       return encoded + weight

   The encoded measurement has a total length of (length + 1) * bits.

   The encoding function encode_range_checked_int is described in
   Section 7.4.2 of [VDAF].

   The encoded 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 [
          decode_range_checked_int(self.field, m, self.max_value)
          for m in chunks(meas, self.max_value.bit_length())
       ]

   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.



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   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]

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 max_value (exclusive) by first
   checking the value is correctly composed from bits, where each bit is
   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 L1 norm.  The
   decoded values are used to recompute the L1 norm as the sum of the
   individual components.  The difference between reported and computed
   values is checked to confirm that the values 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]:
       bits = self.max_value.bit_length()
       assert len(meas) == (self.length + 1) * 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)

       c = chunks(meas, bits)
       components = [
           decode_range_checked_int(self,field, m, self.max_value)
           for m in c[:self.length]
       ]
       observed_weight = sum(components)
       claimed_weight = decode_range_checked_int(
           self.field, c[self.length], self.max_value
       )
       weight_check = observed_weight - claimed_weight

       return [range_check, weight_check]

             Figure 1: Evaluation function for Prio3L1BoundSum

   This evaluation uses the decode_range_checked_int() function defined
   in Section 7.4.2 of [VDAF].

4.  DAP Integration

   The integration of Prio3L1BoundSum in DAP [DAP] requires the
   definition of an encoding for the configuration of the VDAF.
   Figure 2 defines the encoding of Prio3L1BoundSumConfig, using the
   syntax definitions from Section 3 of [RFC8446].






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   struct {
     uint32 length;
     uint32 max_value;
     uint32 chunk_length;
   } Prio3L1BoundSumConfig;

         Figure 2: VDAF Configuration Encoding for Prio3L1BoundSum

   This configuration is three 32-bit integers, each in network byte
   order, with semantics described in Section 3, as follows:

   length:  The total number of values in each measurement.

   max_value:  The maximum value, inclusive, for the sum of all
      measurement values.

   chunk_length:  The size of each chunk used in the evaluation circuit;
      see Figure 1.

5.  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.

6.  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)

7.  References

7.1.  Normative References





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   [DAP]      Geoghegan, T., Patton, C., Pitman, B., Rescorla, E., and
              C. A. Wood, "Distributed Aggregation Protocol for Privacy
              Preserving Measurement", Work in Progress, Internet-Draft,
              draft-ietf-ppm-dap-17, 30 January 2026,
              <https://datatracker.ietf.org/doc/html/draft-ietf-ppm-dap-
              17>.

   [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>.

   [RFC8446]  Rescorla, E., "The Transport Layer Security (TLS) Protocol
              Version 1.3", RFC 8446, DOI 10.17487/RFC8446, August 2018,
              <https://www.rfc-editor.org/rfc/rfc8446>.

   [VDAF]     Barnes, R., Cook, D., Patton, C., and P. Schoppmann,
              "Verifiable Distributed Aggregation Functions", Work in
              Progress, Internet-Draft, draft-irtf-cfrg-vdaf-18, 30
              January 2026, <https://datatracker.ietf.org/doc/html/
              draft-irtf-cfrg-vdaf-18>.

7.2.  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
   Email: mt@lowentropy.net


   David Cook
   ISRG



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   Email: divergentdave@gmail.com


















































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