Network Working Group A. Davidson
Internet-Draft N. Sullivan
Intended status: Informational Cloudflare
Expires: September 10, 2020 C. Wood
Apple Inc.
March 09, 2020
Oblivious Pseudorandom Functions (OPRFs) using Prime-Order Groups
draft-irtf-cfrg-voprf-03
Abstract
An Oblivious Pseudorandom Function (OPRF) is a two-party protocol for
computing the output of a PRF. One party (the server) holds the PRF
secret key, and the other (the client) holds the PRF input. The
'obliviousness' property ensures that the server does not learn
anything about the client's input during the evaluation. The client
should also not learn anything about the server's secret PRF key.
Optionally, OPRFs can also satisfy a notion 'verifiability' (VOPRF).
In this setting, the client can verify that the server's output is
indeed the result of evaluating the underlying PRF with just a public
key. This document specifies OPRF and VOPRF constructions
instantiated within prime-order groups, including elliptic curves.
Status of This Memo
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Copyright Notice
Copyright (c) 2020 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Change log . . . . . . . . . . . . . . . . . . . . . . . 5
1.2. Terminology . . . . . . . . . . . . . . . . . . . . . . . 6
1.3. Requirements . . . . . . . . . . . . . . . . . . . . . . 6
2. Background . . . . . . . . . . . . . . . . . . . . . . . . . 6
3. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . 7
3.1. Security Properties . . . . . . . . . . . . . . . . . . . 7
3.2. Prime-order group instantiation . . . . . . . . . . . . . 8
3.3. Conventions . . . . . . . . . . . . . . . . . . . . . . . 8
3.3.1. Binary strings . . . . . . . . . . . . . . . . . . . 8
3.3.2. Group notation . . . . . . . . . . . . . . . . . . . 9
4. OPRF Protocol . . . . . . . . . . . . . . . . . . . . . . . . 9
4.1. Design . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2. Protocol functionality . . . . . . . . . . . . . . . . . 11
4.2.1. Generalized OPRF . . . . . . . . . . . . . . . . . . 12
4.2.2. Generalized VOPRF . . . . . . . . . . . . . . . . . . 13
4.3. Protocol correctness . . . . . . . . . . . . . . . . . . 14
4.4. Domain separation . . . . . . . . . . . . . . . . . . . . 14
4.5. Instantiations of GG . . . . . . . . . . . . . . . . . . 14
4.6. OPRF algorithms . . . . . . . . . . . . . . . . . . . . . 15
4.6.1. Setup . . . . . . . . . . . . . . . . . . . . . . . . 15
4.6.2. Blind . . . . . . . . . . . . . . . . . . . . . . . . 15
4.6.3. Evaluate . . . . . . . . . . . . . . . . . . . . . . 16
4.6.4. Unblind . . . . . . . . . . . . . . . . . . . . . . . 16
4.6.5. Finalize . . . . . . . . . . . . . . . . . . . . . . 17
4.7. VOPRF algorithms . . . . . . . . . . . . . . . . . . . . 17
4.7.1. VerifiableSetup . . . . . . . . . . . . . . . . . . . 17
4.7.2. VerifiableBlind . . . . . . . . . . . . . . . . . . . 17
4.7.3. VerifiableEvaluate . . . . . . . . . . . . . . . . . 18
4.7.4. VerifiableUnblind . . . . . . . . . . . . . . . . . . 18
4.7.5. VerifiableFinalize . . . . . . . . . . . . . . . . . 19
4.8. Efficiency gains with pre-processing and fixed-base
blinding . . . . . . . . . . . . . . . . . . . . . . . . 19
4.8.1. Preprocess . . . . . . . . . . . . . . . . . . . . . 20
4.8.2. Blind . . . . . . . . . . . . . . . . . . . . . . . . 20
4.8.3. Unblind . . . . . . . . . . . . . . . . . . . . . . . 21
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5. NIZK Discrete Logarithm Equality Proof . . . . . . . . . . . 21
5.1. DLEQ_Generate . . . . . . . . . . . . . . . . . . . . . . 22
5.2. DLEQ_Verify . . . . . . . . . . . . . . . . . . . . . . . 22
6. Batched VOPRF evaluation . . . . . . . . . . . . . . . . . . 23
6.1. Batched_DLEQ_Generate . . . . . . . . . . . . . . . . . . 24
6.2. DLEQ_Batched_Verify . . . . . . . . . . . . . . . . . . . 24
6.3. Modified algorithms . . . . . . . . . . . . . . . . . . . 25
6.3.1. VerifiableBlind . . . . . . . . . . . . . . . . . . . 25
6.3.2. VerifiableEvaluate . . . . . . . . . . . . . . . . . 26
6.3.3. VerifiableUnblind . . . . . . . . . . . . . . . . . . 26
6.3.4. VerifiableFinalize . . . . . . . . . . . . . . . . . 27
6.4. Random oracle instantiations for proofs . . . . . . . . . 27
7. Supported ciphersuites . . . . . . . . . . . . . . . . . . . 28
7.1. OPRF-curve448-HKDF-SHA512-ELL2-RO: . . . . . . . . . . . 28
7.2. OPRF-P384-HKDF-SHA512-SSWU-RO: . . . . . . . . . . . . . 28
7.3. OPRF-P521-HKDF-SHA512-SSWU-RO: . . . . . . . . . . . . . 29
7.4. VOPRF-curve448-HKDF-SHA512-ELL2-RO: . . . . . . . . . . . 29
7.5. VOPRF-P384-HKDF-SHA512-SSWU-RO: . . . . . . . . . . . . . 29
7.6. VOPRF-P521-HKDF-SHA512-SSWU-RO: . . . . . . . . . . . . . 30
8. Security Considerations . . . . . . . . . . . . . . . . . . . 30
8.1. Cryptographic security . . . . . . . . . . . . . . . . . 30
8.1.1. Computational hardness assumptions . . . . . . . . . 30
8.1.2. Protocol security . . . . . . . . . . . . . . . . . . 31
8.1.3. Q-strong-DH oracle . . . . . . . . . . . . . . . . . 32
8.1.4. Implications for ciphersuite choices . . . . . . . . 32
8.2. Hashing to curve . . . . . . . . . . . . . . . . . . . . 33
8.3. Timing Leaks . . . . . . . . . . . . . . . . . . . . . . 33
8.4. User segregation . . . . . . . . . . . . . . . . . . . . 33
8.4.1. Linkage patterns . . . . . . . . . . . . . . . . . . 34
8.4.2. Evaluation on multiple keys . . . . . . . . . . . . . 34
8.5. Key rotation . . . . . . . . . . . . . . . . . . . . . . 35
9. Applications . . . . . . . . . . . . . . . . . . . . . . . . 36
9.1. Privacy Pass . . . . . . . . . . . . . . . . . . . . . . 36
9.2. Private Password Checker . . . . . . . . . . . . . . . . 36
9.2.1. Parameter Commitments . . . . . . . . . . . . . . . . 37
10. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 37
11. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 37
12. References . . . . . . . . . . . . . . . . . . . . . . . . . 37
12.1. Normative References . . . . . . . . . . . . . . . . . . 37
12.2. URIs . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 39
1. Introduction
A pseudorandom function (PRF) F(k, x) is an efficiently computable
function with secret key k on input x. Roughly, F is pseudorandom if
the output y = F(k, x) is indistinguishable from uniformly sampling
any element in F's range for random choice of k. An oblivious PRF
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(OPRF) is a two-party protocol between a prover P and verifier V
where P holds a PRF key k and V holds some input x. The protocol
allows both parties to cooperate in computing F(k, x) with P's secret
key k and V's input x such that: V learns F(k, x) without learning
anything about k; and P does not learn anything about x. A
Verifiable OPRF (VOPRF) is an OPRF wherein P can prove to V that F(k,
x) was computed using key k, which is bound to a trusted public key Y
= kG. Informally, this is done by presenting a non-interactive zero-
knowledge (NIZK) proof of equality between (G, Y) and (Z, M), where Z
= kM for some point M.
OPRFs have been shown to be useful for constructing: password-
protected secret sharing schemes [JKK14]; privacy-preserving password
stores [SJKS17]; and password-authenticated key exchange or PAKE
[OPAQUE]. VOPRFs are useful for producing tokens that are verifiable
by V. This may be needed, for example, if V wants assurance that P
did not use a unique key in its computation, i.e., if V wants key
consistency from P. This property is necessary in some applications,
e.g., the Privacy Pass protocol [PrivacyPass], wherein this VOPRF is
used to generate one-time authentication tokens to bypass CAPTCHA
challenges. VOPRFs have also been used for password-protected secret
sharing schemes e.g. [JKKX16].
This document introduces an OPRF protocol built in prime-order
groups, applying to finite fields of prime-order and also elliptic
curve (EC) settings. The protocol has the option of being extended
to a VOPRF with the addition of a NIZK proof for proving discrete log
equality relations. This proof demonstrates correctness of the
computation using a known public key that serves as a commitment to
the server's secret key. The document describes the protocol, its
security properties, and provides preliminary test vectors for
experimentation. The rest of the document is structured as follows:
o Section 2: Describe background, related work, and use cases of
OPRF/VOPRF protocols.
o Section 3: Describe conventions and assumptions made relating to
security of (V)OPRFs and prime-order group instantiations.
o Section 4: Specify an authentication protocol from OPRF
functionality, based in prime-order groups (with an optional
verifiable mode). Algorithms are stated formally for OPRFs in
Section 4.6 and for VOPRFs in Section 4.7.
o Section 5: Specify the NIZK discrete logarithm equality (DLEQ)
construction used for constructing the VOPRF protocol.
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o Section 6: Specifies how the DLEQ proof mechanism can be batched
for multiple VOPRF invocations, and how this changes the protocol
execution.
o Section 7: Considers explicit instantiations of the protocol in
the elliptic curve setting.
o Section 8: Discusses the security considerations for the OPRF and
VOPRF protocol.
o Section 9: Discusses some existing applications of OPRF and VOPRF
protocols.
1.1. Change log
draft-03 [1]:
o Certify public key during VerifiableFinalize
o Remove protocol integration advice
o Add text discussing how to perform domain separation
o Drop OPRF_/VOPRF_ prefix from algorithm names
o Make prime-order group assumption explicit
o Changes to algorithms accepting batched inputs
o Changes to construction of batched DLEQ proofs
o Updated ciphersuites to be consistent with hash-to-curve and added
OPRF specific ciphersuites
draft-02 [2]:
o Added section discussing cryptographic security and static DH
oracles
o Updated batched proof algorithms
draft-01 [3]:
o Updated ciphersuites to be in line with
https://tools.ietf.org/html/draft-irtf-cfrg-hash-to-curve-04
o Made some necessary modular reductions more explicit
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1.2. Terminology
The following terms are used throughout this document.
o PRF: Pseudorandom Function.
o OPRF: Oblivious PRF.
o VOPRF: Verifiable Oblivious Pseudorandom Function.
o Verifier (V): Protocol initiator when computing F(k, x), also
known as client.
o Prover (P): Holder of secret key k, also known as server.
o NIZK: Non-interactive zero knowledge.
o DLEQ: Discrete Logarithm Equality.
1.3. Requirements
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in [RFC2119].
2. Background
OPRFs are functionally related to blind signature schemes. In such a
scheme, a client can receive signatures on private data, under the
signing key of some server. The security properties of such a scheme
dictate that the client learns nothing about the signing key, and
that the server learns nothing about the data that is signed. One of
the more popular blind signature schemes is based on the RSA
cryptosystem and is known as Blind RSA [ChaumBlindSignature].
OPRF protocols can thought of as symmetric alternatives to blind
signatures. Essentially the client learns y = PRF(k,x) for some
input x of their choice, from a server that holds k. Since the
security of an OPRF means that x is hidden in the interaction, then
the client can later reveal x to the server along with y.
The server can verify that y is computed correctly by recomputing the
PRF on x using k. In doing so, the client provides knowledge of a
'signature' y for their value x. The verification procedure is thus
symmetric as it requires knowledge of the key k. This is discussed
more in the following section.
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3. Preliminaries
We start by detailing some necessary cryptographic definitions.
3.1. Security Properties
The security properties of an OPRF protocol with functionality y =
F(k, x) include those of a standard PRF. Specifically:
o Pseudorandomness: F is pseudorandom if the output y = F(k,x) on
any input x is indistinguishable from uniformly sampling any
element in F's range, for a random sampling of k.
In other words, for an adversary that can pick inputs x from the
domain of F and can evaluate F on (k,x) (without knowledge of
randomly sampled k), then the output distribution F(k,x) is
indistinguishable from the uniform distribution in the range of F.
A consequence of showing that a function is pseudorandom, is that it
is necessarily non-malleable (i.e. we cannot compute a new evaluation
of F from an existing evaluation). A genuinely random function will
be non-malleable with high probability, and so a pseudorandom
function must be non-malleable to maintain indistinguishability.
An OPRF protocol must also satisfy the following property:
o Oblivious: P must learn nothing about V's input or the output of
the function. In addition, V must learn nothing about P's private
key.
Essentially, obliviousness tells us that, even if P learns V's input
x at some point in the future, then P will not be able to link any
particular OPRF evaluation to x. This property is also known as
unlinkability [DGSTV18].
Optionally, for any protocol that satisfies the above properties,
there is an additional security property:
o Verifiable: V must only complete execution of the protocol if it
can successfully assert that the OPRF output computed by V is
correct, with respect to the OPRF key held by P.
Any OPRF that satisfies the 'verifiable' security property is known
as a verifiable OPRF, or VOPRF for short. In practice, the notion of
verifiability requires that P commits to the key k before the actual
protocol execution takes place. Then V verifies that P has used k in
the protocol using this commitment. In the following, we may also
refer to this commitment as a public key.
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3.2. Prime-order group instantiation
In this document, we assume the construction of a prime-order group
GG for performing all mathematical operations. Such a group MUST
provide the interface provided by cyclic group under the addition
operation (for example, well-defined addition of group elements). We
also assume the presence of a fixed generator G that can be detailed
as a fixed parameter in the description of the group. We write p =
order(GG) to represent the order of the group throughout this
document.
It is common in cryptographic applications to instantiate such prime-
order groups using elliptic curves, such as those detailed in [SEC2].
For some choices of elliptic curves (e.g. those detailed in [RFC7748]
require accounting for cofactors) there are some implementation
issues that introduce inherent discrepancies between standard prime-
order groups and the elliptic curve instantiation. In this document,
all algorithms that we detail assume that the group is a prime-order
group, and this MUST be upheld by any implementer. That is, any
curve instantiation should be written such that any discrepancies
with a prime-order group instantiation are removed. In the case of
cofactors, for example, this can be done by building cofactor
multiplication into all elliptic curve operations.
3.3. Conventions
We detail a list of conventions that we use throughout this document.
3.3.1. Binary strings
o We use the notation x <-$ Q to denote sampling x from the uniform
distribution over the set Q.
o We use x <- {0,1}^u to denote sampling x uniformly from the set of
binary strings of length u. We may interpret x afterwards as a
byte array.
o We say that x is a binary string of arbitrary-length (or
alternatively sampled from {0,1}^*) if there is no fixed-size
requirement on x.
o For two byte arrays x & y, write x .. y to denote their
concatenation.
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3.3.2. Group notation
o We use the letter p to denote the order of a group GG throughout,
where the instantiation of the specific group is defined by
context.
o For elements A & B of GG, we write A + B to denote the addition of
thr group elements.
o We use GF(p) to denote the Galois Field of scalar values
associated with the group GG.
o For a scalar r in GF(p), and a group element A, we write rA to
denote the scalar multiplication of A.
o For two scalars r, s in GF(p), we use r+s to denote the resulting
scalar in GF(p) (we may optionally write r+s mod p to make the
modular reduction explicit).
4. OPRF Protocol
In this section we describe the OPRF and VOPRF protocols. Recall
that such a protocol takes place between a verifier (V) and a prover
(P). Commonly, V is a client and P is a server, and so we use these
names interchangeably throughout. We always operate under the
assumption that the verifier is a client, and the prover is a server
in the interaction (and so we will use these names interchangeably
throughout). The server holds a secret key k for a PRF. The
protocol allows the client to learn PRF evaluations on chosen inputs
x in such a way that the server learns nothing of x.
Our OPRF construction is based on the VOPRF construction known as
2HashDH-NIZK given by [JKK14]; essentially without providing zero-
knowledge proofs that verify that the output is correct. Our VOPRF
construction (including the NIZK DLEQ proofs from Section 5) is
identical to the [JKK14] construction. With batched proofs
(Section 6) our construction differs slightly in that we can perform
multiple VOPRF evaluations in one go, whilst only constructing one
NIZK proof object.
In this section we describe the OPRF and VOPRF protocols. Recall
that such a protocol takes place between a verifier (V) and a prover
(P). We may commonly think of the verifier as the client, and the
prover as the server in the interaction (we will use these names
interchangeably throughout). The server holds a key k for a PRF.
The protocol allows the client to learn PRF evaluations on chosen
inputs x without revealing x to the server.
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Our OPRF construction is based on the VOPRF construction known as
2HashDH-NIZK given by [JKK14]; essentially without providing zero-
knowledge proofs that verify that the output is correct. Our VOPRF
construction (including the NIZK DLEQ proofs from Section 5) is
identical to the [JKK14] construction. With batched proofs
(Section 6) our construction differs slightly in that we can perform
multiple VOPRF evaluations in one go, whilst only constructing one
NIZK proof object.
4.1. Design
Let GG be an additive group of prime-order p, let GF(p) be the Galois
field defined by the integers modulo p. Define distinct hash
functions H_1 and H_2, where H_1 maps arbitrary input onto GG (H_1:
{0,1}^* -> GG) and H_2 maps two arbitrary inputs to a fixed-length
(w) output (H_2: {0,1}^u x {0,1}^v -> {0,1}^w), e.g., HMAC_SHA256.
All hash functions in the protocol are modeled as random oracles.
Let L be the security parameter. Let k be the prover's secret key,
and Y = kG be its corresponding 'public key' for some fixed generator
G taken from the description of the group GG. This public key Y is
also referred to as a commitment to the OPRF key k, and the pair
(G,Y) as a commitment pair. Let x be the binary string that is the
verifier's input to the OPRF protocol (this can be of arbitrary
length).
The OPRF protocol begins with V blinding its input for the OPRF
evaluator such that it appears uniformly distributed GG. The latter
then applies its secret key to the blinded value and returns the
result. To finish the computation, V then removes its blind and
hashes the result (along with a domain separating label DST) using
H_2 to yield an output. This flow is illustrated below.
Verifier(x) Prover(k)
----------------------------------------------------------
r <-$ GF(p)
M = rH_1(x) mod p
M
------->
Z = kM mod p
[D = DLEQ_Generate(k,G,Y,M,Z)]
Z[,D]
<-------
[b = DLEQ_Verify(G,Y,M,Z,D)]
N = Zr^(-1) mod p
Output H_2(DST, x .. N) mod p [if b=1, else "error"]
Steps that are enclosed in square brackets (DLEQ_Generate and
DLEQ_Verify) are optional for achieving verifiability. These are
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described in Section 5. In the verifiable mode, we assume that P has
previously committed to their choice of key k with some values
(G,Y=kG) and these are publicly known by V. Notice that revealing
(G,Y) does not reveal k by the well-known hardness of the discrete
log problem.
Strictly speaking, the actual PRF function that is computed is:
F(k, x) = N = kH_1(x)
It is clear that this is a PRF H_1(x) maps x to a random element in
GG, and GG is cyclic. This output is computed when the client
computes Zr^(-1) by the commutativity of the multiplication. The
client finishes the computation by outputting H_2(DST, x .. N). Note
that the output from P is not the PRF value because the actual input
x is blinded by r.
The security of our construction is discussed in more detail in
Section 8.1.2.
4.2. Protocol functionality
This protocol may be decomposed into a series of steps, as described
below:
o Setup(l): Let GG=GG(l) be a group with a prime-order p=p(l) (e.g.,
p is l-bits long). Randomly sample an integer k in GF(p) and
output (k,GG)
o Blind(x): Compute and return a blind, r, and blinded
representation of x in GG, denoted M.
o Evaluate(k,M,h?): Evaluates on input M using secret key k to
produce Z, the input h is optional and equal to the cofactor of an
elliptic curve. If h is not provided then it defaults to 1.
o Unblind(r,Z): Unblind blinded OPRF evaluation Z with blind r,
yielding N and output N.
o Finalize(x,N,aux?): Finalize N by first computing dk := H_2(DST, x
.. N). Subsequently output y := H_2(dk, aux), where aux is some
auxiliary data encoded as a byte string. If aux is not specified,
it defaults to the empty byte string.
For verifiability (VOPRF) we modify the algorithms of
VerifiableSetup, VerifiableEvaluate and VerifiableUnblind to be the
following:
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o VerifiableSetup(l): Run (k,GG) = Setup(l), compute Y = kG, where G
is a generator of the group GG. Output (k,GG,Y).
o VerifiableEvaluate(k,G,Y,M,h?): Evaluates on input M using secret
key k to produce Z. Generate a NIZK proof D =
DLEQ_Generate(k,G,Y,M,Z), and output (Z, D). The optional
cofactor h can also be provided, as in Evaluate.
o VerifiableUnblind(r,G,Y,M,Z,D): Unblind blinded OPRF evaluation Z
with blind r, yielding N. Output N if 1 = DLEQ_Verify(G,Y,M,Z,D).
Otherwise, output "error".
o VerifiableFinalize(x,Y,N,aux?): Same as Finalize, except we now
compute dk := H_2(DST, x .. Y .. N), i.e. we also certify the
public key in the finalization process.
We leave the rest of the OPRF algorithms unmodified. When referring
explicitly to VOPRF execution, we replace 'OPRF' in all method names
with 'VOPRF'. We describe explicit instantiations of these functions
in Section 4.6 and Section 4.7.
4.2.1. Generalized OPRF
Using the API provided by the functions above, we can restate the
OPRF protocol using the following descriptions. The first protocol
refers to the OPRF setup phase that is run by the server. This
generates the secret input used by the server and the public
information that is given to the client.
OPRF setup phase:
Verifier() Prover(l)
----------------------------------------------------------
(k,GG) = Setup(l)
GG
<-------
OPRF evaluation phase:
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Verifier(x,aux) Prover(k)
----------------------------------------------------------
(r, M) = Blind(x)
M
------->
Z = Evaluate(k,M)
Z
<-------
N = Unblind(r,Z)
Output Finalize(x,N,aux)
Note that in the final output, the client computes Finalize over some
auxiliary input data aux.
4.2.2. Generalized VOPRF
The generalized VOPRF functionality differs slightly from the OPRF
protocol above. Firstly, the server sends over an extra commitment
value Y = kG, where G is a common generator known to both
participants. Secondly, the server sends over both outputs from
VerifiableEvaluate in the evaluation phase, and the client also
verifies the server's output.
VOPRF setup phase:
Verifier() Prover(l)
----------------------------------------------------------
(k,GG,Y) = VerifiableSetup(l)
(GG,Y)
<-------
VOPRF evaluation phase:
Verifier(x,Y,aux) Prover(k)
----------------------------------------------------------
(r, M) = VerifiableBlind(x)
M
------->
(Z,D) = VerifiableEvaluate(k,G,Y,M)
(Z,D)
<-------
N = VerifiableUnblind(r,G,Y,M,Z,D)
Output VerifiableFinalize(x,Y,N,aux)
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4.3. Protocol correctness
Protocol correctness requires that, for any key k, input x, and (r,
M) = Blind(x), it must be true that:
Finalize(x, Unblind(r,M,Evaluate(k,M)), aux)
== H_2(H_2(DST, x .. F(k,x)), aux)
with overwhelming probability. Likewise, in the verifiable setting,
we require that:
Z = VerifiableEvaluate(k,G,Y,M)
VerifiableFinalize(x, Y, VerifiableUnblind(r,G,Y,M,Z), aux)
== H_2(H_2(DST, x .. F(k,x)), aux)
with overwhelming probability, where (r, M) = VerifiableBlind(x). In
other words, the inner H_2 invocation effectively derives a key, dk,
from the input data DST, x, N. The outer invocation derives the
output y by evaluating H_2 over dk and auxiliary data aux.
4.4. Domain separation
The Finalize procedure accepts optional auxiliary byte string input
(aux) as a means of modifying the PRF output. This parameter SHOULD
be used for domain separation in (V)OPRF the protocol. Specifically,
any system which has multiple (V)OPRF applications should use
separate aux values to to ensure finalized outputs are separate.
Guidance for constructing aux can be found in
[I-D.irtf-cfrg-hash-to-curve]; Section 3.1.
4.5. Instantiations of GG
As we remarked above, GG is a group with associated prime-order p.
While we choose to write operations in the setting where GG comes
equipped with an additive operation, we could also define the
operations in the multiplicative setting. In the multiplicative
setting we can choose GG to be a prime-order subgroup of a finite
field FF_p. For example, let p be some large prime (e.g. > 2048
bits) where p = 2q+1 for some other prime q. Then the subgroup of
squares of FF_p (elements u^2 where u is an element of FF_p) is
cyclic, and we can pick a generator of this subgroup by picking G
from FF_p (ignoring the identity element).
For practicality of the protocol, it is preferable to focus on the
cases where GG is an additive subgroup so that we can instantiate the
OPRF in the elliptic curve setting. This amounts to choosing GG to
be a prime-order subgroup of an elliptic curve over base field GF(p)
for prime p. There are also other settings where GG is a prime-order
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subgroup of an elliptic curve over a base field of non-prime order,
these include the work of Ristretto [RISTRETTO] and Decaf [DECAF].
We will use p > 0 generally for constructing the base field GF(p),
not just those where p is prime. To reiterate, we focus only on the
additive case, and so we focus only on the cases where GF(p) is
indeed the base field.
Unless otherwise stated, we will always assume that the generator G
that we use for the group GG is a fixed generator. This generator
should be available to both the client and the server ahead of the
protocol, or derived for each different group instantiation using a
fixed method. In the elliptic curve setting, we recommend using the
fixed generators that are given as part of the curve description.
4.6. OPRF algorithms
This section provides descriptions of the algorithms used in the
generalized protocols from Section 4.2.1. We describe the VOPRF
analogues for the protocols in Section 4.2.2 later in Section 4.7.
We note here that the blinding mechanism that we use can be modified
slightly with the opportunity for making performance gains in some
scenarios. We detail these modifications in Section Section 4.8.
4.6.1. Setup
Input:
l: Some suitable choice of prime length for instantiating a group
structure (e.g. as described in [NIST]).
Output:
k: A key chosen from {0,1}^l and interpreted as a scalar in [1,p-1].
GG: A cyclic group with prime-order p of length l bits.
Steps:
1. Construct a group GG = GG(l) with prime-order p of length l bits
2. k <-$ GF(p)
3. Output (k,GG)
4.6.2. Blind
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Input:
x: Binary string taken from {0,1}^*.
Output:
r: Random scalar in [1, p - 1].
M: An element in GG.
Steps:
1. r <-$ GF(p)
2. M := rH_1(x)
3. Output (r, M)
4.6.3. Evaluate
Input:
k: A scalar value taken from [1,p-1].
M: An element in GG.
Output:
Z: An element in GG.
Steps:
1. Z := kM
2. Output Z
4.6.4. Unblind
Input:
r: Random scalar in [1, p - 1].
Z: An element in GG.
Output:
N: An element in GG.
Steps:
1. N := (r^(-1))Z
2. Output N
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4.6.5. Finalize
Input:
x: Binary string taken from {0,1}^*.
N: An element in GG.
aux: Arbitrary auxiliary data (as bytes).
Output:
y: Random element in {0,1}^L.
Steps:
1. DST := "oprf_derive_output"
2. dk := H_2(DST, x .. N)
3. y := H_2(dk, aux)
4. Output y
4.7. VOPRF algorithms
We make modifications to the aforementioned algorithms in the VOPRF
setting.
4.7.1. VerifiableSetup
Input:
G: Public fixed generator of GG.
l: Some suitable choice of key-length (e.g. as described in [NIST]).
Output:
k: A key chosen from {0,1}^l and interpreted as a scalar in [1,p-1].
GG: A cyclic group with prime-order p of length l bits.
Y: A group element in GG.
Steps:
1. (k,GG) <- Setup(l)
2. Y := kG
3. Output (k,GG,Y)
4.7.2. VerifiableBlind
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Input:
x: V's PRF input.
Output:
r: Random scalar in [1, p - 1].
M: An element in GG.
Steps:
1. r <-$ GF(p)
2. M := rH_1(x)
3. Output (r,M)
4.7.3. VerifiableEvaluate
Input:
k: A random scalar in [1,p-1].
G: Public fixed generator of group GG.
Y: An element in GG.
M: An element in GG.
Output:
Z: An element in GG.
D: DLEQ proof that log_G(Y) == log_M(Z).
Steps:
1. Z := kM
2. Z <- hZ
3. D = DLEQ_Generate(k,G,Y,M,Z)
4. Output (Z, D)
4.7.4. VerifiableUnblind
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Input:
r: Random scalar in [1, p - 1].
G: Public fixed generator of group GG.
Y: An element in GG.
M: An element in GG.
Z: An element in GG.
D: DLEQ proof object.
Output:
N: An element in GG.
Steps:
1. if DLEQ_Verify(G,Y,M,Z,D) == false: output "error"
2. N := (r^(-1))Z
3. Output N
4.7.5. VerifiableFinalize
Input:
x: Binary string in {0,1}^*.
Y: An element in GG.
N: An element in GG, or "error".
aux: Arbitrary auxiliary data in {0,1}^*.
Output:
y: Random element in {0,1}^L, or "error"
Steps:
1. If N == "error", output "error".
2. DST := "voprf_derive_output"
3. dk := H_2(DST, x .. Y .. N)
4. y := H_2(dk, aux)
5. Output y
4.8. Efficiency gains with pre-processing and fixed-base blinding
In Section Section 4.6 we assume that the client-side blinding is
carried out directly on the output of H_1(x), i.e. computing rH_1(x)
for some r <-$ GF(p). In the [OPAQUE] draft, it is noted that it may
be more efficient to use additive blinding rather than multiplicative
if the client can preprocess some values. For example, a valid way
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of computing additive blinding would be to instead compute H_1(x)+rG,
where G is the fixed generator for the group GG.
We refer to the 'multiplicative' blinding as variable-base blinding
(VBB), since the base of the blinding (H_1(x)) varies with each
instantiation. We refer to the additive blinding case as fixed-base
blinding (FBB) since the blinding is applied to the same generator
each time (when computing rG).
By pre-processing tables of blinded scalar multiplications for the
specific choice of G it is possible to gain a computational
advantage. Choosing one of these values rG (where r is the scalar
value that is used), then computing H_1(x)+rG is more efficient than
computing rH_1(x) (one addition against log_2(r)). Therefore, it may
be advantageous to define the OPRF and VOPRF protocols using additive
blinding rather than multiplicative blinding. In fact, the only
algorithms that need to change are Blind and Unblind (and similarly
for the VOPRF variants).
We define the FBB variants of the algorithms in Section 4.6 below
along with a new algorithm Preprocess that defines how preprocessing
is carried out. The equivalent algorithms for VOPRF are almost
identical and so we do not redefine them here. Notice that the only
computation that changes is for V, the necessary computation of P
does not change.
4.8.1. Preprocess
Input:
G: Public fixed generator of GG
Output:
r: Random scalar in [1, p-1]
rG: An element in GG.
rY: An element in GG.
Steps:
1. r <-$ GF(p)
2. Output (r, rG, rY)
4.8.2. Blind
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Input:
x: Binary string in {0,1}^*.
rG: An element in GG.
Output:
M: An element in GG.
Steps:
1. M := H_1(x)+rG
2. Output M
4.8.3. Unblind
Input:
rY: An element in GG.
M: An element in GG.
Z: An element in GG.
Output:
N: An element in GG.
Steps:
1. N := Z-rY
2. Output N
Notice that Unblind computes (Z-rY) = k(H_1(x)+rG) - rkG = kH_1(x) by
the commutativity of scalar multiplication in GG. This is the same
output as in the original Unblind algorithm.
5. NIZK Discrete Logarithm Equality Proof
For the VOPRF protocol we require that V is able to verify that P has
used its private key k to evaluate the PRF. We can do this by
showing that the original commitment (G,Y) output by
VerifiableSetup(l) satisfies log_G(Y) == log_M(Z) where Z is the
output of VerifiableEvaluate(k,G,Y,M).
This may be used, for example, to ensure that P uses the same private
key for computing the VOPRF output and does not attempt to "tag"
individual verifiers with select keys. This proof must not reveal
the P's long-term private key to V.
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Consequently, this allows extending the OPRF protocol with a (non-
interactive) discrete logarithm equality (DLEQ) algorithm built on a
Chaum-Pedersen [ChaumPedersen] proof. This proof is divided into two
procedures: DLEQ_Generate and DLEQ_Verify. These are specified
below.
5.1. DLEQ_Generate
Input:
k: Evaluator secret key.
G: Public fixed generator of GG.
Y: Evaluator public key (= kG).
M: An element in GG.
Z: An element in GG.
H_3: A hash function from GG to {0,1}^L, modeled as a random oracle.
Output:
D: DLEQ proof (c, s).
Steps:
1. r <-$ GF(p)
2. A := rG
3. B := rM
4. c <- H_3(G,Y,M,Z,A,B) (mod p)
5. s := (r - ck) (mod p)
6. Output D := (c, s)
We note here that it is essential that a different r value is used
for every invocation. If this is not done, then this may leak the
key k in a similar fashion as is possible in Schnorr or (EC)DSA
scenarios where fresh randomness is not used.
5.2. DLEQ_Verify
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Input:
G: Public fixed generator of GG.
Y: Evaluator public key.
M: An element in GG.
Z: An element in GG.
D: DLEQ proof (c, s).
Output:
True if log_G(Y) == log_M(Z), False otherwise.
Steps:
1. A' := (sG + cY)
2. B' := (sM + cZ)
3. c' <- H_3(G,Y,M,Z,A',B') (mod p)
4. Output c == c' (mod p)
6. Batched VOPRF evaluation
Common applications (e.g. [PrivacyPass]) require V to obtain
multiple PRF evaluations from P. In the VOPRF case, this would
naively require running multiple protocol invocations. This is
costly, both in terms of computation and communication. To get
around this, applications can use a 'batching' procedure for
generating and verifying DLEQ proofs for a finite number of PRF
evaluation pairs (Mi,Zi). For n PRF evaluations:
o Proof generation is slightly more expensive from 2n modular
exponentiations to 2n+2.
o Proof verification is much more efficient, from 4n modular
exponentiations to 2n+4.
o Communications falls from 2n to 2 group elements.
Since P is the VOPRF server, it may be able to tolerate a slight
increase in proof generation complexity for much more efficient
communication and proof verification.
In this section, we describe algorithms for batching the DLEQ
generation and verification procedure. For these algorithms we
require two additional hash functions H_4: GG^(2n+2) -> {0,1}^a, and
H_5: {0,1}^a x ZZ^3 -> {0,1}^b (both modeled as random oracles).
We can instantiate the random oracle function H_4 using the same hash
function that is used for H_3 previously. For H_5, we can also use a
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similar instantiation, or we can use a variable-length output
generator. For example, for groups with an order of 256-bit, valid
instantiations include functions such as SHAKE-256 [SHAKE] or HKDF-
Expand-SHA256 [RFC5869]. This is preferable in situations where we
may require outputs that are larger than 512 bits in length, for
example.
6.1. Batched_DLEQ_Generate
Input:
k: Evaluator secret key.
G: Public fixed generator of group GG (with order p).
Y: Evaluator public key (= kG).
n: Number of PRF evaluations.
[ Mi ]: An array of points in GG of length n.
[ Zi ]: An array of points in GG of length n.
H_4: A random oracle hash function from GG^(2n+2) to {0,1}^a.
H_5: A random oracle hash function from {0,1}^a x ZZ^2 to {0,1}^b.
label: An integer label value for the splitting the domain of H_5
Output:
D: DLEQ proof (c, s).
Steps:
1. seed <- H_4(G,Y,[Mi,Zi]))
2. i' := i
3. for i in [m]:
1. di <- H_5(seed,i',info)
2. if di > p:
1. i' = i'+1
2. i = i-1 // decrement and try again
3. continue
4. c1,...,cn := (int)d1,...,(int)dn
5. M := c1M1 + ... + cnMn
6. Z := c1Z1 + ... + cnZn
7. Output DLEQ_Generate(k,G,Y,M,Z)
6.2. DLEQ_Batched_Verify
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Input:
G: Public fixed generator of group GG (with order p).
Y: Evaluator public key.
[ Mi ]: An array of points in GG of length n.
[ Zi ]: An array of points in GG of length n.
D: DLEQ proof (c, s).
Output:
True if log_G(Y) == log_(Mi)(Zi) for each i in 1...n, False otherwise.
Steps:
1. seed <- H_4(G,Y,[Mi,Zi]))
2. i' := i
3. for i in [m]:
1. di <- H_5(seed,i',info)
2. if di > p:
1. i' = i'+1
2. i = i-1 // decrement and try again
3. continue
4. c1,...,cn := (int)d1,...,(int)dn
5. M := c1M1 + ... + cnMn
6. Z := c1Z1 + ... + cnZn
7. Output DLEQ_Verify(G,Y,M,Z,D)
6.3. Modified algorithms
The VOPRF protocol from Section Section 4 changes to allow specifying
multiple blinded PRF inputs "[ Mi ]" for i in 1...n. P computes the
array "[ Zi]" and replaces DLEQ_Generate with DLEQ_Batched_Generate
over these arrays. Concretely, we modify the following algorithms:
6.3.1. VerifiableBlind
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Input:
[ xi ]: An array of m binary strings taken from {0,1}^*.
Output:
[ ri ]: An array of m random scalars in [1, p - 1].
[ Mi ]: An array of elements in GG.
Steps:
1. groupElems = []
2. blinds = []
3. for i in [m]:
1. ri <-$ GF(p)
2. Mi := rH_1(xi)
3. blinds.push(ri)
4. groupElems.push(Mi)
4. Output (blinds, groupElems)
6.3.2. VerifiableEvaluate
Input:
k: Evaluator secret key.
G: Public fixed generator of group GG.
Y: Evaluator public key (= kG).
[ Mi ]: An array of m elements in GG.
Output:
[ Zi ]: An array of m elements in GG.
D: Batched DLEQ proof object.
Steps:
1. outputElems = []
2. for i in [m]:
1. Zi := kMi
2. outputElems.push(Zi)
3. D = Batched_DLEQ_Generate(k,G,Y,[ Mi ],outputElems)
4. Output (outputElems, D)
6.3.3. VerifiableUnblind
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Input:
G: Public fixed generator of group GG.
Y: Evaluator public key (= kG).
[ Mi ]: An array of m elements in GG.
[ Zi ]: An array of m elements in GG.
[ ri ]: An array of m random scalars in [1, p - 1].
D: Batched DLEQ proof object.
Output:
[ Ni ]: An array of n elements in GG.
Steps:
1. if !Batch_DLEQ_Verify(G,Y,[ Mi ],[ Zi ],D): Output "error"
2. N = []
3. for i in [m]:
1. Ni := (ri^(-1))Zi
2. N.push(Ni)
4. Output N
6.3.4. VerifiableFinalize
The description of this algorithm does not change in the batched
case. Instead, the protocol description in Section 4.2.2 changes so
that "VerifiableFinalize" runs once for each of the outputs of
"VerifiableUnblind".
6.4. Random oracle instantiations for proofs
We can instantiate the random oracle function H_4 using the same hash
function that is used for H_1,H_2,H_3. For H_5, we can also use a
similar instantiation, or we can use a variable-length output
generator. For example, for groups with an order of 256-bit, valid
instantiations include functions such as SHAKE-256 [SHAKE] or HKDF-
Expand-SHA256 [RFC5869].
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Input:
[ ri ]: Random scalars in [1, p - 1].
G: Public fixed generator of group GG.
Y: Evaluator public key.
[ Mi ]: Blinded elements of GG.
[ Zi ]: Server-generated elements in GG.
D: A batched DLEQ proof object.
Output:
N: element in GG, or "error".
Steps:
1. N := (r^(-1))Z
2. If 1 = DLEQ_Batched_Verify(G,Y,[ Mi ],[ Zi ],D), output N
3. Output "error"
7. Supported ciphersuites
This section specifies supported VOPRF group and hash function
instantiations. We only provide ciphersuites in the EC setting as
these provide the most efficient way of instantiating the OPRF. Our
instantiation includes considerations for providing the DLEQ proofs
that make the instantiation a VOPRF. Supporting OPRF operations
alone can be allowed by simply dropping the relevant components. For
reasons that are detailed in Section 8.1, we only consider
ciphersuites that provide strictly greater than 128 bits of security
[NIST].
7.1. OPRF-curve448-HKDF-SHA512-ELL2-RO:
o GG: curve448 [RFC7748]
o H_1: curve448-SHA512-ELL2-RO [I-D.irtf-cfrg-hash-to-curve]
* hash-to-curve DST: "RFCXXXX-OPRF-curve448-SHA512-ELL2-RO-"
o H_2: HMAC_SHA512 [RFC2104]
o H_3: SHA512
7.2. OPRF-P384-HKDF-SHA512-SSWU-RO:
o GG: secp384r1 [SEC2]
o H_1: P384-SHA512-SSWU-RO [I-D.irtf-cfrg-hash-to-curve]
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* hash-to-curve DST: "RFCXXXX-OPRF-P384-SHA512-SSWU-RO-"
o H_2: HMAC_SHA512 [RFC2104]
o H_3: SHA512
7.3. OPRF-P521-HKDF-SHA512-SSWU-RO:
o GG: secp521r1 [SEC2]
o H_1: P521-SHA512-SSWU-RO [I-D.irtf-cfrg-hash-to-curve]
* hash-to-curve DST: "RFCXXXX-OPRF-P521-SHA512-SSWU-RO-"
o H_2: HMAC_SHA512 [RFC2104]
o H_3: SHA512
7.4. VOPRF-curve448-HKDF-SHA512-ELL2-RO:
o GG: curve448 [RFC7748]
o H_1: curve448-SHA512-ELL2-RO [I-D.irtf-cfrg-hash-to-curve]
* hash-to-curve DST: "RFCXXXX-VOPRF-curve448-SHA512-ELL2-RO-"
o H_2: HMAC_SHA512 [RFC2104]
o H_3: SHA512
o H_4: SHA512
o H_5: HKDF-Expand-SHA512
7.5. VOPRF-P384-HKDF-SHA512-SSWU-RO:
o GG: secp384r1 [SEC2]
o H_1: P384-SHA512-SSWU-RO [I-D.irtf-cfrg-hash-to-curve]
* hash-to-curve DST: "RFCXXXX-VOPRF-P384-SHA512-SSWU-RO-"
o H_2: HMAC_SHA512 [RFC2104]
o H_3: SHA512
o H_4: SHA512
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o H_5: HKDF-Expand-SHA512
7.6. VOPRF-P521-HKDF-SHA512-SSWU-RO:
o GG: secp521r1 [SEC2]
o H_1: P521-SHA512-SSWU-RO [I-D.irtf-cfrg-hash-to-curve]
* hash-to-curve DST: "RFCXXXX-VOPRF-P521-SHA512-SSWU-RO-"
o H_2: HMAC_SHA512 [RFC2104]
o H_3: SHA512
o H_4: SHA512
o H_5: HKDF-Expand-SHA512
We remark that the 'hash-to-curve DST' field is necessary for domain
separation of the hash-to-curve functionality.
8. Security Considerations
This section discusses the cryptographic security of our protocol,
along with some suggestions and trade-offs that arise from the
implementation of the implementation of an OPRF.
8.1. Cryptographic security
We discuss the cryptographic security of the OPRF protocol from
Section 4, relative to the necessary cryptographic assumptions that
need to be made.
8.1.1. Computational hardness assumptions
Each assumption states that the problems specified below are
computationally difficult to solve in relation to sp (the security
parameter). In other words, the probability that an adversary has in
solving the problem is bounded by a function negl(sp), where negl(sp)
< 1/f(sp) for all polynomial functions f().
Let GG = GG(sp) be a group with prime-order p, and let FFp be the
finite field of order p.
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8.1.1.1. Discrete-log (DL) problem
Given G, a generator of GG, and H = hG for some h in FFp; output h.
8.1.1.2. Decisional Diffie-Hellman (DDH) problem
Sample a uniformly random bit d in {0,1}. Given (G, aG, bG, C),
where:
o G is a generator of GG;
o a,b are elements of FFp;
o if d == 0: C = abG; else: C is sampled uniformly GG(sp).
Output d' == d.
8.1.2. Protocol security
As aforementioned, our OPRF and VOPRF constructions are based heavily
on the 2HashDH-NIZK construction given in [JKK14], except for
considerations on how we instantiate the NIZK DLEQ proof system.
This means that the cryptographic security of our construction is
also based on the assumption that the One-More Gap DH is
computationally difficult to solve.
The (N,Q)-One-More Gap DH (OMDH) problem asks the following.
Given:
- G, kG, G_1, ... , G_N where G, G1, ... GN are elements od GG;
- oracle access to an OPRF functionality using the key k;
- oracle access to DDH solvers.
Find Q+1 pairs of the form below:
(G_{j_s}, kG_{j_s})
where the following conditions hold:
- s is a number between 1 and Q+1;
- j_s is a number between 1 and N for each s;
- Q is the number of allowed queries.
The original paper [JKK14] gives a security proof that the 2HashDH-
NIZK construction satisfies the security guarantees of a VOPRF
protocol Section 3.1 under the OMDH assumption in the universal
composability (UC) security model. Without the NIZK proof system,
the protocol instantiates an OPRF protocol only. See the paper for
further details.
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8.1.3. Q-strong-DH oracle
A side-effect of our OPRF design is that it allows instantiation of a
oracle for constructing Q-strong-DH (Q-sDH) samples. The Q-Strong-DH
problem asks the following.
Given G1, G2, h*G2, (h^2)*G2, ..., (h^Q)*G2; for G1 and G2
generators of GG.
Output ( (1/(k+c))*G1, c ) where c is an element of FFp
The assumption that this problem is hard was first introduced in
[BB04]. Since then, there have been a number of cryptanalytic
studies that have reduced the security of the assumption below that
implied by the group instantiation (for example, [BG04] and
[Cheon06]). In summary, the attacks reduce the security of the group
instantiation by log_2(Q) bits.
As an example, suppose that a group instantiation is used that
provides 128 bits of security. Then an adversary with access to a
Q-sDH oracle and makes Q=2^20 queries can reduce the security of the
instantiation by log_2(2^20) = 20 bits.
Notice that it is easy to instantiate a Q-sDH oracle using the OPRF
functionality that we provide. A client can just submit sequential
queries of the form (G, kG, (k^2)G, ..., (k^(Q-1))G), where each
query is the output of the previous interaction. This means that any
client that submit Q queries to the OPRF can use the aforementioned
attacks to reduce security of the group instantiation by log_2(Q)
bits.
Recall that from a malicious client's perspective, the adversary wins
if they can distinguish the OPRF interaction from a protocol that
computes the ideal functionality provided by the PRF.
8.1.4. Implications for ciphersuite choices
The OPRF instantiations that we recommend in this document are
informed by the cryptanalytic discussion above. In particular,
choosing elliptic curves configurations that describe 128-bit group
instantiations would appear to in fact instantiate an OPRF with
128-log_2(Q) bits of security.
While it would require an informed and persistent attacker to launch
a highly expensive attack to reduce security to anything much below
100 bits of security, we see this possibility as something that may
result in problems in the future. Therefore, all of our ciphersuites
in Section 7 come with a minimum group instantiation corresponding to
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196 bits of security. This would require an adversary to launch a
minimum of Q = 2^(68) queries to reduce security to 128 bits using
the Q-sDH attacks. As a result, it appears prohibitively expensive
to launch credible attacks on these parameters with our current
understanding of the attack surface.
8.2. Hashing to curve
A critical aspect of implementing this protocol using elliptic curve
group instantiations is a method of instantiating the function H1,
that maps inputs to group elements. In the elliptic curve setting,
this must be a deterministic function that maps arbitrary inputs x
(as bytes) to uniformly chosen points in the curve.
In the security proof of the construction H1 is modeled as a random
oracle. This implies that any instantiation of H1 must be pre-image
and collision resistant. In Section 7 we give instantiations of this
functionality based on the functions described in
[I-D.irtf-cfrg-hash-to-curve]. Consequently, any OPRF implementation
must adhere to the implementation and security considerations
discussed in [I-D.irtf-cfrg-hash-to-curve] when instantiating the
function H1.
8.3. Timing Leaks
To ensure no information is leaked during protocol execution, all
operations that use secret data MUST be constant time. Operations
that SHOULD be constant time include: H_1() (hashing arbitrary
strings to curves) and DLEQ_Generate(). As mentioned previously,
[I-D.irtf-cfrg-hash-to-curve] describes various algorithms for
constant-time implementations of H_1.
8.4. User segregation
The aim of the OPRF functionality is to allow clients receive
pseudorandom function evaluations on their own inputs, without
compromising their own privacy with respect to the server. In many
applications (for example, [PrivacyPass]) the client may choose to
reveal their original input, after an invocation of the OPRF
protocol, along with their OPRF output. This can prove to the server
that it has received a valid OPRF output in the past. Since the
server does not reveal learn anything about the OPRF output, it
should not be able to link the client to any previous protocol
instantiation.
Consider a malicious server that manages to segregate the user base
into different sets. Then this reduces the effective privacy of all
of the clients involved, since the client above belongs to a smaller
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set of users than previously hoped. In general, if the user-base of
the OPRF functionality is quite small, then the obliviousness of
clients is limited. That is, smaller user-bases mean that the server
is able to identify client's with higher certainty.
In summary, an OPRF instantiation effectively comes with an
additional privacy parameter pp. If all clients of the OPRF make one
query and then subsequently reveal their OPRF input afterwards, then
the server should be link the revealed input to a protocol
instantiation with probability 1/pp.
Below, we provide a few techniques that could be used to abuse
client-privacy in the OPRF construction by segregating the user-base,
along with some mitigations.
8.4.1. Linkage patterns
If the server is able to ascertain patterns of usage for some clients
- such as timings associated with usage - then the effective privacy
of the clients is reduced to the number of users that fit each usage
pattern. Along with early registration patterns, where early
adopters initially have less privacy due to a low number of
registered users, such problems are inherent to any anonymity-
preserving system.
8.4.2. Evaluation on multiple keys
Such an attack consists of the server evaluating the OPRF on multiple
different keys related to the number of clients that use the
functionality. As an extreme, the server could evaluate the OPRF
with a different key for each client. If the client then revealed
their hidden information at a later date then the server would
immediately know which initial request they launched.
The VOPRF variant helps mitigate this attack since each server
evaluation can be bound to a known public key. However, there are
still ways that the VOPRF construction can be abused. In particular:
o If the server successfully provisions a large number of keys that
are trusted by clients, then the server can divide the user-base
by the number of keys that are currently in use. As such, clients
should only trust a small number (2 or 3 ideally) of server keys
at any one time. Additionally, a tamper-proof audit log system
akin to existing work on Key Transparency [keytrans] could be used
to ensure that a server is abiding by the key policy. This would
force the server to be held accountable for their key updates, and
thus higher key update frequencies can be better managed on the
client-side.
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o If the server rotates their key frequently, then this may result
in client's holding out-of-date information from a past
interaction. Such information can also be used to segregate the
user-base based on the last time that they accessed the OPRF
protocol. Similarly to the above, server key rotations must be
kept to relatively infrequent intervals (such as once per month).
This will prevent too many clients from being segregated into
different groups related to the time that they accessed the
functionality. There are viable reasons for rotating the server
key (for protecting against malicious clients) that we address
more closely in Section 8.5.
Since key provisioning requires careful handling, all public keys
should be accessible from a client-trusted registry with a way of
auditing the history of key updates. We also recommend that public
keys have a corresponding expiry date that clients can use to prevent
the server from using keys that have been provisioned for a long
period of time.
8.5. Key rotation
Since the server's key is critical to security, the longer it is
exposed by performing (V)OPRF operations on client inputs, the longer
it is possible that the key can be compromised. For instance, if the
key is kept in production for a long period of time, then this may
grant the client the ability to hoard large numbers of tokens. This
has negative impacts for some of the applications that we consider in
Section 9. As another example, if the key is kept in circulation for
a long period of time, then it also allows the clients to make enough
queries to launch more powerful variants of the Q-sDH attacks from
Section 8.1.3.
To combat attacks of this nature, regular key rotation should be
employed on the server-side. A suitable key-cycle for a key used to
compute (V)OPRF evaluations would be between one week and six months.
As we discussed in Section 8.4.2, key rotation cycles that are too
frequent (in the order of days) can lead to large segregation of the
wider user base. As such, the length of the key cycles represent a
trade-off between greater server key security (for shorter cycles),
and better client privacy (for longer cycles). In situations where
client privacy is paramount, longer key cycles should be employed.
Otherwise, shorter key cycles can be managed if the server uses a Key
Transparency-type system [keytrans]; this allows clients to publicly
audit their rotations.
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9. Applications
This section describes various applications of the (V)OPRF protocol.
9.1. Privacy Pass
This VOPRF protocol is used by the Privacy Pass system [PrivacyPass]
to help Tor users bypass CAPTCHA challenges. Their system works as
follows. Client C connects - through Tor - to an edge server E
serving content. Upon receipt, E serves a CAPTCHA to C, who then
solves the CAPTCHA and supplies, in response, n blinded points. E
verifies the CAPTCHA response and, if valid, signs (at most) n
blinded points, which are then returned to C along with a batched
DLEQ proof. C stores the tokens if the batched proof verifies
correctly. When C attempts to connect to E again and is prompted
with a CAPTCHA, C uses one of the unblinded and signed points, or
tokens, to derive a shared symmetric key sk used to MAC the CAPTCHA
challenge. C sends the CAPTCHA, MAC, and token input x to E, who can
use x to derive sk and verify the CAPTCHA MAC. Thus, each token is
used at most once by the system.
The Privacy Pass implementation uses the P-256 instantiation of the
VOPRF protocol. For more details, see [DGSTV18].
9.2. Private Password Checker
In this application, let D be a collection of plaintext passwords
obtained by prover P. For each password p in D, P computes
VerifiableEvaluate on H_1(p), where H_1 is as described above, and
stores the result in a separate collection D'. P then publishes D'
with Y, its public key. If a client C wishes to query D' for a
password p', it runs the VOPRF protocol using p as input x to obtain
output y. By construction, y will be the OPRF evaluation of p hashed
onto the curve. C can then search D' for y to determine if there is
a match.
Concrete examples of important applications in the password domain
include:
o password-protected storage [JKK14], [JKKX16];
o perfectly-hiding password management [SJKS17];
o password-protected secret-sharing [JKKX17].
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9.2.1. Parameter Commitments
For some applications, it may be desirable for P to bind tokens to
certain parameters, e.g., protocol versions, ciphersuites, etc. To
accomplish this, P should use a distinct scalar for each parameter
combination. Upon redemption of a token T from V, P can later verify
that T was generated using the scalar associated with the
corresponding parameters.
10. Contributors
o Alex Davidson (alex.davidson92@gmail.com)
o Nick Sullivan (nick@cloudflare.com)
o Chris Wood (cawood@apple.com)
o Eli-Shaoul Khedouri (eli@intuitionmachines.com)
11. Acknowledgements
This document resulted from the work of the Privacy Pass team
[PrivacyPass]. The authors would also like to acknowledge the
helpful conversations with Hugo Krawczyk. Eli-Shaoul Khedouri
provided additional review and comments on key consistency.
12. References
12.1. Normative References
[BB04] "Short Signatures Without Random Oracles",
.
[BG04] "The Static Diffie-Hellman Problem",
.
[ChaumBlindSignature]
"Blind Signatures for Untraceable Payments",
.
[ChaumPedersen]
"Wallet Databases with Observers",
.
[Cheon06] "Security Analysis of the Strong Diffie-Hellman Problem",
.
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[DECAF] "Decaf, Eliminating cofactors through point compression",
.
[DGSTV18] "Privacy Pass, Bypassing Internet Challenges Anonymously",
.
[I-D.irtf-cfrg-hash-to-curve]
Faz-Hernandez, A., Scott, S., Sullivan, N., Wahby, R., and
C. Wood, "Hashing to Elliptic Curves", draft-irtf-cfrg-
hash-to-curve-05 (work in progress), November 2019.
[JKK14] "Round-Optimal Password-Protected Secret Sharing and
T-PAKE in the Password-Only model",
.
[JKKX16] "Highly-Efficient and Composable Password-Protected Secret
Sharing (Or, How to Protect Your Bitcoin Wallet Online)",
.
[JKKX17] "TOPPSS: Cost-minimal Password-Protected Secret Sharing
based on Threshold OPRF",
.
[keytrans]
"Security Through Transparency",
.
[NIST] "Keylength - NIST Report on Cryptographic Key Length and
Cryptoperiod (2016)", .
[OPAQUE] "The OPAQUE Asymmetric PAKE Protocol",
.
[PrivacyPass]
"Privacy Pass",
.
[RFC2104] Krawczyk, H., Bellare, M., and R. Canetti, "HMAC: Keyed-
Hashing for Message Authentication", RFC 2104,
DOI 10.17487/RFC2104, February 1997,
.
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[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
.
[RFC5869] Krawczyk, H. and P. Eronen, "HMAC-based Extract-and-Expand
Key Derivation Function (HKDF)", RFC 5869,
DOI 10.17487/RFC5869, May 2010,
.
[RFC7748] Langley, A., Hamburg, M., and S. Turner, "Elliptic Curves
for Security", RFC 7748, DOI 10.17487/RFC7748, January
2016, .
[RISTRETTO]
"The ristretto255 Group", .
[SEC2] Standards for Efficient Cryptography Group (SECG), ., "SEC
2: Recommended Elliptic Curve Domain Parameters",
.
[SHAKE] "SHA-3 Standard, Permutation-Based Hash and Extendable-
Output Functions", .
[SJKS17] "SPHINX, A Password Store that Perfectly Hides from
Itself", .
12.2. URIs
[1] https://tools.ietf.org/html/draft-irtf-cfrg-voprf-03
[2] https://tools.ietf.org/html/draft-irtf-cfrg-voprf-02
[3] https://tools.ietf.org/html/draft-irtf-cfrg-voprf-01
Authors' Addresses
Alex Davidson
Cloudflare
County Hall
London, SE1 7GP
United Kingdom
Email: adavidson@cloudflare.com
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Nick Sullivan
Cloudflare
101 Townsend St
San Francisco
United States of America
Email: nick@cloudflare.com
Christopher A. Wood
Apple Inc.
One Apple Park Way
Cupertino, California 95014
United States of America
Email: cawood@apple.com
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