Network Working Group A. Davidson
Internet-Draft N. Sullivan
Intended status: Informational C. Wood
Expires: January 14, 2021 Cloudflare
July 13, 2020
Oblivious Pseudorandom Functions (OPRFs) using Prime-Order Groups
draft-irtf-cfrg-voprf-04
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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provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on January 14, 2021.
Copyright Notice
Copyright (c) 2020 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
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Change log . . . . . . . . . . . . . . . . . . . . . . . 3
1.2. Terminology . . . . . . . . . . . . . . . . . . . . . . . 5
1.3. Requirements . . . . . . . . . . . . . . . . . . . . . . 5
2. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1. Prime-order group API . . . . . . . . . . . . . . . . . . 5
2.2. Other conventions . . . . . . . . . . . . . . . . . . . . 6
3. OPRF Protocol . . . . . . . . . . . . . . . . . . . . . . . . 7
3.1. Overview . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2. Context Setup . . . . . . . . . . . . . . . . . . . . . . 8
3.3. Data Structures . . . . . . . . . . . . . . . . . . . . . 9
3.4. Context APIs . . . . . . . . . . . . . . . . . . . . . . 10
3.4.1. Server Context . . . . . . . . . . . . . . . . . . . 10
3.4.2. VerifiableServerContext . . . . . . . . . . . . . . . 12
3.4.3. Client Context . . . . . . . . . . . . . . . . . . . 16
3.4.4. VerifiableClientContext . . . . . . . . . . . . . . . 17
4. Ciphersuites . . . . . . . . . . . . . . . . . . . . . . . . 19
4.1. OPRF(curve25519, SHA-512) . . . . . . . . . . . . . . . . 20
4.2. OPRF(curve448, SHA-512) . . . . . . . . . . . . . . . . . 20
4.3. OPRF(P-256, SHA-512) . . . . . . . . . . . . . . . . . . 21
4.4. OPRF(P-384, SHA-512) . . . . . . . . . . . . . . . . . . 22
4.5. OPRF(P-521, SHA-512) . . . . . . . . . . . . . . . . . . 23
5. Security Considerations . . . . . . . . . . . . . . . . . . . 23
5.1. Security properties . . . . . . . . . . . . . . . . . . . 24
5.2. Cryptographic security . . . . . . . . . . . . . . . . . 25
5.2.1. Computational hardness assumptions . . . . . . . . . 25
5.2.2. Protocol security . . . . . . . . . . . . . . . . . . 25
5.2.3. Q-strong-DH oracle . . . . . . . . . . . . . . . . . 26
5.2.4. Implications for ciphersuite choices . . . . . . . . 27
5.3. Hashing to curve . . . . . . . . . . . . . . . . . . . . 27
5.4. Timing Leaks . . . . . . . . . . . . . . . . . . . . . . 27
5.5. Key rotation . . . . . . . . . . . . . . . . . . . . . . 28
6. Additive blinding . . . . . . . . . . . . . . . . . . . . . . 28
6.1. Preprocess . . . . . . . . . . . . . . . . . . . . . . . 29
6.2. Blind . . . . . . . . . . . . . . . . . . . . . . . . . . 29
6.3. Unblind . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.3.1. Parameter Commitments . . . . . . . . . . . . . . . . 31
7. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 31
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8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 31
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 31
9.1. Normative References . . . . . . . . . . . . . . . . . . 31
9.2. URIs . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 34
1. Introduction
A pseudorandom function (PRF) F(k, x) is an efficiently computable
function taking a private key k and a value x as input. This
function is pseudorandom if the keyed function K(_) = F(K, _) is
indistinguishable from a randomly sampled function acting on the same
domain and range as K(). An oblivious PRF (OPRF) is a two-party
protocol between a server and a client, where the server holds a PRF
key k and the client holds some input x. The protocol allows both
parties to cooperate in computing F(k, x) such that: the client
learns F(k, x) without learning anything about k; and the server does
not learn anything about x. A Verifiable OPRF (VOPRF) is an OPRF
wherein the server can prove to the client that F(k, x) was computed
using the key k.
The usage of OPRFs has been demonstrated in constructing a number of
applications: password-protected secret sharing schemes [JKKX16];
privacy-preserving password stores [SJKS17]; and password-
authenticated key exchange or PAKE [OPAQUE]. The usage of a VOPRF is
necessary in some applications, e.g., the Privacy Pass protocol
[draft-davidson-pp-protocol], 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. [JKK14].
This document introduces an OPRF protocol built in prime-order
groups, applying to finite fields of prime-order and also elliptic
curve (EC) groups. 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, the
public-facing API, and its security properties.
1.1. Change log
draft-04 [1]:
o Introduce Client and Server contexts for controlling verifiability
and required functionality.
o Condense API.
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o Remove batching from standard functionality (included as an
extension)
o Add Curve25519 and P-256 ciphersuites for applications that
prevent strong-DH oracle attacks.
o Provide explicit prime-order group API and instantiation advice
for each ciphersuite.
o Proof-of-concept implementation in sage.
o Remove privacy considerations advice as this depends on
applications.
draft-03 [2]:
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 [3]:
o Added section discussing cryptographic security and static DH
oracles
o Updated batched proof algorithms
draft-01 [4]:
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 Pseudorandom Function.
o VOPRF: Verifiable Oblivious Pseudorandom Function.
o Client: Protocol initiator. Learns pseudorandom function
evaluation as the output of the protocol.
o Server: Computes the pseudorandom function over a secret key.
Learns nothing about the client's input.
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. Preliminaries
2.1. Prime-order group API
In this document, we assume the construction of an additive, prime-
order group "GG" for performing all mathematical operations. Such
groups are uniquely determined by the choice of the prime "p" that
defines the order of the group. We use "GF(p)" to represent the
finite field of order "p". For the purpose of understanding and
implementing this document, we take "GF(p)" to be equal to the set of
integers defined by "{0, 1, ..., p-1}".
The fundamental group operation is addition "+" with identity element
"I". For any elements "A" and "B" of the group "GG", "A + B = B + A"
is also a member of "GG". Also, for any "A" in "GG", it exists an
element "-A" such that "A + (-A) = (-A) + A = I". Scalar
multiplication is equivalent to the repeated application of the group
operation on an element A with itself "r-1" times, this is denoted as
"r*A = A + ... + A". Any element "A" holds the equality "p*A=I".
The set of scalars corresponds to "GF(p)".
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We now detail a number of member functions that can be invoked on a
prime-order group.
o Order(): Outputs the order of the group (i.e. "p").
o Generator(): Outputs a fixed generator "G" for the group.
o Identity(): Outputs the identity element of the group (i.e. "I").
o Serialize(A): A member function of "GG" that maps a group element
"A" to a unique byte array "buf".
o Deserialize(buf): A member function of "GG" that maps a byte array
"buf" to a group element "A".
o HashToGroup(x): A member function of "GG" that deterministically
maps an array of bytes "x" to an element of "GG". The map must
ensure that, for any adversary receiving "R = HashToGroup(x)", it
is computationally difficult to reverse the mapping. Examples of
hash to group functions satisfying this property are described for
prime-order (sub)groups of elliptic curves, see
[I-D.irtf-cfrg-hash-to-curve].
o HashToScalar(x): A member function of "GG" that deterministically
maps an array of bytes "x" to a random element in GF(p).
o RandomScalar(): A member function of "GG" that generates a random,
non-zero element in GF(p).
It is convenient 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.
See Section 4 for advice corresponding to implementation of this
interface for specific definitions of elliptic curves.
2.2. Other conventions
o We use the notation "x <-$ Q" to denote sampling "x" from the
uniform distribution over the set "Q".
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o For any object "x", we write "len(x)" to denote its length in
bytes.
o For two byte arrays "x" and "y", write "x || y" to denote their
concatenation.
o We assume that all numbers are stored in big-endian orientation.
o I2OSP and OS2IP: Convert a byte array to and from a non-negative
integer as described in [RFC8017]. Note that these functions
operate on byte arrays in big-endian byte order.
All algorithm descriptions are written in a Python-like pseudocode.
We use the "ABORT()" function for presentational clarity to denote
the process of terminating the algorithm or returning an error
accordingly. We also use the "CT_EQUAL(a, b)" function to represent
constant-time byte-wise equality between byte arrays "a" and "b".
This function returns a boolean "true" if "a" and "b" are equal, and
"false" otherwise.
3. OPRF Protocol
In this section, we define two OPRF variants: a base mode and
verifiable mode. In the base mode, a client and server interact to
compute y = F(skS, x), where x is the client's input, skS is the
server's private key, and y is the OPRF output. The client learns y
and the server learns nothing. In the verifiable mode, the client
also gets proof that the server used skS in computing the function.
To achieve verifiability, as in the original work of [JKK14], we
provide a zero-knowledge proof that the key provided as input by the
server in the "Evaluate" function is the same key as it used to
produce their public key. As an example of the nature of attacks
that this prevents, this ensures that the server uses the same
private key for computing the VOPRF output and does not attempt to
"tag" individual servers with select keys. This proof must not
reveal the server's long-term private key to the client.
The following one-byte values distinguish between these two modes:
+----------+-------+---+----------------+------+
| Mode | Value | | | |
+----------+-------+---+----------------+------+
| modeBase | 0x00 | | modeVerifiable | 0x01 |
+----------+-------+---+----------------+------+
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3.1. Overview
Both participants agree on the mode and a choice of ciphersuite that
is used before the protocol exchange. Once established, the core
protocol runs to compute "output = F(skS, input)" as follows:
Client(pkS, input, info) Server(skS, pkS)
----------------------------------------------------------
token, blindToken = Blind(input)
blindToken
---------->
evaluation = Evaluate(skS, pkS, blindToken)
evaluation
<----------
issuedToken = Unblind(pkS, token, blindToken, evaluation)
output = Finalize(input, issuedToken, info)
In "Blind" the client generates a token and blinding data. The
server computes the (V)OPRF evaluation in "Evaluation" over the
client's blinded token. In "Unblind" the client unblinds the server
response (and verifies the server's proof if verifiability is
required). In "Finalize", the client outputs a byte array
corresponding to its input.
Note that in the final output, the client computes Finalize over some
auxiliary input data "info". This parameter SHOULD be used for
domain separation in the (V)OPRF protocol. Specifically, any system
which has multiple (V)OPRF applications should use separate auxiliary
values to to ensure finalized outputs are separate. Guidance for
constructing info can be found in [I-D.irtf-cfrg-hash-to-curve];
Section 3.1.
3.2. Context Setup
Both modes of the OPRF involve an offline setup phase. In this
phase, both the client and server create a context used for executing
the online phase of the protocol. The base mode setup functions for
creating client and server contexts are below:
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def SetupBaseserver(suite):
(skS, _) = KeyGen(GG)
contextString = I2OSP(modeBase, 1) + I2OSP(suite.ID, 2)
return ServerContext(contextString, skS)
def SetupBaseClient(suite):
contextString = I2OSP(modeBase, 1) + I2OSP(suite.ID, 2)
return ClientContext(contextString)
The "KeyGen" function used above takes a group "GG" and generates a
private and public key pair (skX, pkX), where skX is a random, non-
zero element in the scalar field "GG" and pkX is the product of skX
and the group's fixed generator.
The verifiable mode setup functions for creating client and server
contexts are below.
def SetupVerifiableserver(suite):
(skS, pkS) = KeyGen(GG)
contextString = I2OSP(modeVerifiable, 1) + I2OSP(suite.ID, 2)
return VerifiableServerContext(contextString, skS), pkS
def SetupVerifiableClient(suite, pkS):
contextString = I2OSP(modeVerifiable, 1) + I2OSP(suite.ID, 2)
return VerifiableClientContext(contextString, pkS)
For verifiable modes, servers MUST make the resulting public key
"pkS" accessible for clients. (Indeed, it is a required parameter
when configuring a verifiable client context.)
Each setup function takes a ciphersuite from the list defined in
Section 4. Each ciphersuite has two-byte identifier, referred to as
"suite.ID" in the pseudocode above, that identifies the suite.
Section 4 lists these ciphersuite identifiers.
3.3. Data Structures
The following is a list of data structures that are defined for
providing inputs and outputs for each of the context interfaces
defined in Section 3.4.
The following types are a list of aliases that are used throughout
the protocol.
A "ClientInput" is a byte array.
opaque ClientInput<1..2^16-1>;
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A "SerializedElement" is also a byte array, representing the unique
serialization of an "Element".
opaque SerializedElement<1..2^16-1>;
A "Token" is an object created by a client when constructing a
(V)OPRF protocol input. It is stored so that it can be used after
receiving the server response.
struct {
opaque data<1..2^16-1>;
Scalar blind<1..2^16-1>;
} Token;
An "Evaluation" is the type output by the "Evaluate" algorithm. The
member "proof" is added only in verifiable contexts.
struct {
SerializedElement element;
Scalar proof<0...2^16-1>; /* only for modeVerifiable */
} Evaluation;
Evaluations may also be combined in batches with a constant-size
proof, producing a "BatchedEvaluation". These carry a list of
"SerializedElement" values and proof. These evaluation types are
only useful in verifiable contexts which carry proofs.
struct {
SerializedElement elements<1..2^16-1>;
Scalar proof<0...2^16-1>; /* only for modeVerifiable */
} BatchedEvaluation;
3.4. Context APIs
In this section, we detail the APIs available on the client and
server OPRF contexts. This document uses the types "Element" and
"Scalar" to denote elements of the group "GG" and its underlying
scalar field, respectively. For notational clarity, "PublicKey" and
"PrivateKey" are items of type "Element" and "Scalar", respectively.
3.4.1. Server Context
The ServerContext encapsulates the context string constructed during
setup and the OPRF key pair. It has two functions, "Evaluate" and
"VerifyFinalize", described below. "Evaluate" takes as input
serialized representations of blinded group elements from the client.
"VerifyFinalize" takes ClientInput values and their corresponding
output digests from "Verify" and returns true if the inputs match the
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outputs. Note that "VerifyFinalize" is not used in the main OPRF
protocol. It is exposed as an API for building higher-level
protocols.
3.4.1.1. Evaluate
Input:
PrivateKey skS
PublicKey pkS
SerializedElement blindedToken
Output:
Evaluation Ev
def Evaluate(skS, pkS, blindedToken):
BT = GG.Deserialize(blindedToken)
Z = skS * BT
serializedElement = GG.Serialize(Z)
Ev = Evaluation{ element: serializedElement }
return Ev
3.4.1.2. VerifyFinalize
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Input:
PrivateKey skS
PublicKey pkS
ClientInput input
opaque info<1..2^16-1>
opaque output<1..2^16-1>
Output:
boolean valid
def VerifyFinalize(skS, pkS, input, info, output):
T = GG.HashToGroup(input)
element = GG.Serialize(T)
issuedElement = Evaluate(skS, pkS, [element])
E = GG.Serialize(issuedElement)
finalizeDST = "RFCXXXX-Finalize-" + client.contextString
hashInput = len(input) || input ||
len(E) || E ||
len(info) || info ||
len(finalizeDST) || finalizeDST
digest = Hash(hashInput)
return CT_EQUAL(digest, output)
3.4.2. VerifiableServerContext
The VerifiableServerContext extends the base ServerContext with an
augmented "Evaluate()" function. This function produces a proof that
"skS" was used in computing the result. It makes use of the helper
functions "ComputeComposites" and "GenerateProof", described below.
3.4.2.1. Evaluate
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Input:
PrivateKey skS
PublicKey pkS
SerializedElement blindedToken
Output:
Evaluation Ev
def Evaluate(skS, pkS, blindedToken):
BT = GG.Deserialize(blindedToken)
Z = skS * BT
serializedElement = GG.Serialize(Z)
proof = GenerateProof(skS, pkS, blindedToken, serializedElement)
Ev = Evaluation{ element: serializedElement, proof: proof }
return Ev
The helper functions "GenerateProof" and "ComputeComposites" are
defined below.
3.4.2.2. GenerateProof
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Input:
PrivateKey skS
PublicKey pkS
SerializedElement blindedToken
SerializedElement element
Output:
Scalar proof[2]
def GenerateProof(skS, pkS, blindedToken, element)
G = GG.Generator()
gen = GG.Serialize(G)
blindTokenList = [blindedToken]
elementList = [element]
(a1, a2) = ComputeComposites(gen, pkS, blindTokenList, elementList)
M = GG.Deserialize(a1)
r = GG.RandomScalar()
a3 = GG.Serialize(r * G)
a4 = GG.Serialize(r * M)
challengeDST = "RFCXXXX-challenge-" + self.contextString
h2Input = I2OSP(len(gen), 2) || gen ||
I2OSP(len(pkS), 2) || pkS ||
I2OSP(len(a1), 2) || a1 || I2OSP(len(a2), 2) || a2 ||
I2OSP(len(a3), 2) || a3 || I2OSP(len(a4), 2) || a4 ||
I2OSP(len(challengeDST), 2) || challengeDST
c = GG.HashToScalar(h2Input)
s = (r - c * skS) mod p
return (c, s)
3.4.2.2.1. Batching inputs
Unlike other functions, "ComputeComposites" takes lists of inputs,
rather than a single input. It is optimized to produce a constant-
size output. This functionality lets applications batch inputs
together to produce a constant-size proofs from "GenerateProof".
Applications can take advantage of this functionality by invoking
"GenerateProof" on batches of inputs. (Notice that in the pseudocode
above, the single inputs "blindedToken" and "element" are translated
into lists before invoking "ComputeComposites". A batched
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"GenerateProof" variant would permit lists of inputs, and no list
translation would be needed.)
Note that using batched inputs creates a "BatchedEvaluation" object
as the output of "Evaluate".
3.4.2.2.2. Fresh randomness
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 "skS"
as is possible in Schnorr or (EC)DSA scenarios where fresh randomness
is not used.
3.4.2.3. ComputeComposites
Input:
SerializedElement gen
PublicKey pkS
SerializedElement blindedTokens[m]
SerializedElement elements[m]
Output:
SerializedElement composites[2]
def ComputeComposites(gen, pkS, blindedTokens, elements):
seedDST = "RFCXXXX-seed-" + self.contextString
compositeDST = "RFCXXXX-composite-" + self.contextString
h1Input = I2OSP(len(gen), 2) || gen ||
I2OSP(len(pkS), 2) || pkS ||
I2OSP(len(blindedTokens), 2) || blindedTokens ||
I2OSP(len(elements), 2) || elements ||
I2OSP(len(seedDST), 2) || seedDST
seed = Hash(h1Input)
M = GG.Identity()
Z = GG.Identity()
for i = 0 to m:
h2Input = I2OSP(len(seed), 2) || seed || I2OSP(i, 2) ||
I2OSP(len(compositeDST), 2) || compositeDST
di = GG.HashToScalar(h2Input)
Mi = GG.Deserialize(blindedTokens[i])
Zi = GG.Deserialize(elements[i])
M = di * Mi + M
Z = di * Zi + Z
return [GG.Serialize(M), GG.Serialize(Z)]
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3.4.3. Client Context
The ClientContext encapsulates the context string constructed during
setup. It has three functions, "Blind()", "Unblind()", and
"Finalize()", as described below.
3.4.3.1. Blind
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 6.
Input:
ClientInput input
Output:
Token token
SerializedElement blindedToken
def Blind(input):
r = GG.RandomScalar()
P = GG.HashToGroup(input)
blindedToken = GG.Serialize(r * P)
token = Token{ data: input, blind: r }
return (token, blindedToken)
3.4.3.2. Unblind
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Input:
PublicKey pkS
Token token
SerializedElement blindedToken
Evaluation Ev
Output:
SerializedElement unblindedToken
def Unblind(pkS, token, blindedToken, Ev):
r = token.blind
Z = GG.Deserialize(Ev.element)
N = (r^(-1)) * Z
unblindedToken = GG.Serialize(N)
return unblindedToken
3.4.3.3. Finalize
Input:
Token T
SerializedElement E
opaque info<1..2^16-1>
Output:
opaque output<1..2^16-1>
def Finalize(T, E, info):
finalizeDST = "RFCXXXX-Finalize-" + self.contextString
hashInput = len(T.data) || T.data ||
len(E) || E ||
len(info) || info ||
len(finalizeDST) || finalizeDST
return Hash(hashInput)
3.4.4. VerifiableClientContext
The VerifiableClientContext extends the base ClientContext with the
desired server public key "pkS" with an augmented "Unblind()"
function. This function verifies an evaluation proof using "pkS".
It makes use of the helper function "ComputeComposites" described
above. It has one helper function, "VerifyProof()", defined below.
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3.4.4.1. VerifyProof
This algorithm outputs a boolean "verified" which indicates whether
the proof inside of the evaluation verifies correctly, or not.
Input:
PublicKey pkS
SerializedElement blindedToken
Evaluation Ev
Output:
boolean verified
def VerifyProof(pkS, blindedToken, Ev):
G = GG.Generator()
gen = GG.Serialize(G)
blindTokenList = [blindedToken]
elementList = [Ev.element]
(a1, a2) = ComputeComposites(gen, pkS, blindTokenList, elementList)
A' = (Ev.proof[1] * G + Ev.proof[0] * pkS)
B' = (Ev.proof[1] * M + Ev.proof[0] * Z)
a3 = GG.Serialize(A')
a4 = GG.Serialize(B')
challengeDST = "RFCXXXX-challenge-" + self.contextString
h2Input = I2OSP(len(gen), 2) || gen ||
I2OSP(len(pkS), 2) || pkS ||
I2OSP(len(a1), 2) || a1 ||
I2OSP(len(a2), 2) || a2 ||
I2OSP(len(a3), 2) || a3 ||
I2OSP(len(a4), 2) || a4 ||
I2OSP(len(challengeDST), 2) || challengeDST
c = GG.HashToScalar(h2Input)
return CT_EQUAL(c, Ev.proof[0])
3.4.4.2. Unblind
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Input:
PublicKey pkS
Token token
SerializedElement blindedToken
Evaluation Ev
Output:
SerializedElement unblindedToken
def Unblind(pkS, token, blindedToken, Ev):
if VerifyProof(pkS, blindedToken, Ev) == false:
ABORT()
r = token.blind
Z = GG.Deserialize(Ev.element)
N = (r^(-1)) * Z
unblindedToken = GG.Serialize(N)
return unblindedToken
4. Ciphersuites
A ciphersuite for the protocol wraps the functionality required for
the protocol to take place. This ciphersuite should be available to
both the client and server, and agreement on the specific
instantiation is assumed throughout. A ciphersuite contains
instantiations of the following functionalities.
o "GG": A prime-order group exposing the API detailed in
Section 2.1.
o "Hash": A cryptographic hash function that is indifferentiable
from a Random Oracle.
This section specifies supported VOPRF group and hash function
instantiations. For each group, we specify the HashToGroup and
Serialize functionalities. The Deserialize functionality is the
inverse of the corresponding Serialize functionality.
We only provide ciphersuites in the elliptic curve setting as these
provide the most efficient way of instantiating the OPRF.
Applications should take caution in using ciphersuites targeting
P-256 and curve25519. See Section 5.2 for related discussion.
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[[OPEN ISSUE: Replace Curve25519 and Curve448 with Ristretto/Decaf]]
4.1. OPRF(curve25519, SHA-512)
o Group:
* Elliptic curve: curve25519 [RFC7748]
* Generator(): Return the point with the following affine
coordinates:
+ x = "09"
+ y = "5F51E65E475F794B1FE122D388B72EB36DC2B28192839E4DD6163A5
D81312C14"
* HashToGroup(): curve25519_XMD:SHA-512_ELL2_RO_
[I-D.irtf-cfrg-hash-to-curve] with DST "RFCXXXX-
curve25519_XMD:SHA-512_ELL2_RO_"
* Serialization: The standard 32-byte representation of the
public key [RFC7748]
* Order(): Returns "1000000000000000000000000000000014DEF9DEA2F79
CD65812631A5CF5D3ED"
* Addition: Adding curve points directly corresponds to the group
addition operation.
* Deserialization: Implementers must check for each untrusted
input point whether it's a member of the big prime-order
subgroup of the curve. This can be done by scalar multiplying
the point by Order() and checking whether it's zero.
o Hash: SHA-512
o ID: 0x0001
4.2. OPRF(curve448, SHA-512)
o Group:
* Elliptic curve: curve448 [RFC7748]
* Generator(): Return the point with the following affine
coordinates:
+ x = "05"
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+ y = "7D235D1295F5B1F66C98AB6E58326FCECBAE5D34F55545D060F75DC
28DF3F6EDB8027E2346430D211312C4B150677AF76FD7223D457B5B1A"
* HashToGroup(): curve448_XMD:SHA-512_ELL2_RO_
[I-D.irtf-cfrg-hash-to-curve] with DST "RFCXXXX-
curve448_XMD:SHA-512_ELL2_RO_"
* Serialization: The standard 56-byte representation of the
public key [RFC7748]
* Order(): Returns "3FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
FFFFFFFFFFF7CCA23E9C44EDB49AED63690216CC2728DC58F552378C292AB58
44F3"
* Addition: Adding curve points directly corresponds to the group
addition operation.
* Deserialization: Implementers must check for each untrusted
input point whether it's a member of the big prime-order
subgroup of the curve. This can be done by scalar multiplying
the point by Order() and checking whether it's zero.
o Hash: SHA-512
o ID: 0x0002
4.3. OPRF(P-256, SHA-512)
o Group:
* Elliptic curve: P-256 (secp256r1) [x9.62]
* Generator(): Return the point with the following affine
coordinates:
+ x = "6B17D1F2E12C4247F8BCE6E563A440F277037D812DEB33A0F4A1394
5D898C296"
+ y = "4FE342E2FE1A7F9B8EE7EB4A7C0F9E162BCE33576B315ECECBB6406
837BF51F5"
* HashToGroup(): P256_XMD:SHA-256_SSWU_RO_
[I-D.irtf-cfrg-hash-to-curve] with DST "RFCXXXX-P256_XMD:SHA-
256_SSWU_RO_"
* Serialization: The compressed point encoding for the curve
[SEC1] consisting of 33 bytes.
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* Order(): Returns "FFFFFFFF00000000FFFFFFFFFFFFFFFFBCE6FAADA7179
E84F3B9CAC2FC632551"
* Addition: Adding curve points directly corresponds to the group
addition operation.
* Scalar multiplication: Scalar multiplication of curve points
directly corresponds with scalar multiplication in the group.
o Hash: SHA-512
o ID: 0x0003
4.4. OPRF(P-384, SHA-512)
o Group:
* Elliptic curve: P-384 (secp384r1) [x9.62]
* Generator(): Return the point with the following affine
coordinates:
+ x = "AA87CA22BE8B05378EB1C71EF320AD746E1D3B628BA79B9859F741E
082542A385502F25DBF55296C3A545E3872760AB7"
+ y = "3617DE4A96262C6F5D9E98BF9292DC29F8F41DBD289A147CE9DA311
3B5F0B8C00A60B1CE1D7E819D7A431D7C90EA0E5F"
* HashToGroup(): P384_XMD:SHA-512_SSWU_RO_
[I-D.irtf-cfrg-hash-to-curve] with DST "RFCXXXX-P384_XMD:SHA-
512_SSWU_RO_"
* Serialization: The compressed point encoding for the curve
[SEC1] consisting of 49 bytes.
* Order(): Returns "FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
FFFC7634D81F4372DDF581A0DB248B0A77AECEC196ACCC52973"
* Addition: Adding curve points directly corresponds to the group
addition operation.
* Scalar multiplication: Scalar multiplication of curve points
directly corresponds with scalar multiplication in the group.
o Hash: SHA-512
o ID: 0x0004
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4.5. OPRF(P-521, SHA-512)
o Group:
* Elliptic curve: P-521 (secp521r1) [x9.62]
* Generator(): Return the point with the following affine
coordinates:
+ x = "00C6858E06B70404E9CD9E3ECB662395B4429C648139053FB521F82
8AF606B4D3DBAA14B5E77EFE75928FE1DC127A2FFA8DE3348B3C1856A429
BF97E7E31C2E5BD66"
+ y = "011839296A789A3BC0045C8A5FB42C7D1BD998F54449579B446817A
FBD17273E662C97EE72995EF42640C550B9013FAD0761353C7086A272C24
088BE94769FD16650"
* HashToGroup(): P521_XMD:SHA-512_SSWU_RO_
[I-D.irtf-cfrg-hash-to-curve] with DST "RFCXXXX-P521_XMD:SHA-
512_SSWU_RO_"
* Serialization: The compressed point encoding for the curve
[SEC1] consisting of 67 bytes.
* Order(): Returns "1FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
FFFFFFFFFFFFFFFFFFFFFA51868783BF2F966B7FCC0148F709A5D03BB5C9B88
99C47AEBB6FB71E91386409"
* Addition: Adding curve points directly corresponds to the group
addition operation.
* Scalar multiplication: Scalar multiplication of curve points
directly corresponds with scalar multiplication in the group.
o Hash: SHA-512
o ID: 0x0005
5. Security Considerations
This section discusses the cryptographic security of our protocol,
along with some suggestions and trade-offs that arise from the
implementation of an OPRF.
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5.1. 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, consider an adversary that picks inputs x from the
domain of F and evaluates F on (k,x) (without knowledge of randomly
sampled k). Then the output distribution F(k,x) is indistinguishable
from the output distribution of a randomly chosen function with the
same domain and range.
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: The server must learn nothing about the client's input
or the output of the function. In addition, the client must learn
nothing about the server's private key.
Essentially, obliviousness tells us that, even if the server learns
the client's input x at some point in the future, then the server
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: The client must only complete execution of the
protocol if it can successfully assert that the OPRF output it
computes is correct. This is taken with respect to the OPRF key
held by the server.
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 the server commits to the key before the
actual protocol execution takes place. Then the client verifies that
the server has used the key in the protocol using this commitment.
In the following, we may also refer to this commitment as a public
key.
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5.2. Cryptographic security
Below, we discuss the cryptographic security of the (V)OPRF protocol
from Section 3, relative to the necessary cryptographic assumptions
that need to be made.
5.2.1. Computational hardness assumptions
Each assumption states that the problems specified below are
computationally difficult to solve in relation to a particular choice
of security parameter "sp".
Let GG = GG(sp) be a group with prime-order p, and let FFp be the
finite field of order p.
5.2.1.1. Discrete-log (DL) problem
Given G, a generator of GG, and H = hG for some h in FFp; output h.
5.2.1.2. 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.
5.2.2. Protocol security
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 is identical to the [JKK14] construction, except that we
can optionally perform multiple VOPRF evaluations in one go, whilst
only constructing one NIZK proof object. This is enabled using an
established batching technique.
Consequently the cryptographic security of our construction is 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.
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Given:
- G, k * G, G_1, ... , G_N where G, G_1, ... G_N are elements of 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}, k * G_{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 5.1 under the OMDH assumption in the universal
composability (UC) security model.
5.2.3. 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 against discrete log cryptanalysis.
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, k * G, (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
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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.
5.2.4. 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.
In most cases, 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. For
applications that cannot tolerate discrete logarithm security of
lower than 128 bits, we recommend only implementing ciphersuites with
IDs: 0x0002, 0x0004, and 0x0005.
5.3. Hashing to curve
A critical requirement of implementing the prime-order group using
elliptic curves is a method to instantiate the function
"GG.HashToGroup", that maps inputs to group elements. In the
elliptic curve setting, this deterministically maps inputs x (as byte
arrays) to uniformly chosen points in the curve.
In the security proof of the construction Hash is modeled as a random
oracle. This implies that any instantiation of "GG.HashToGroup" must
be pre-image and collision resistant. In Section 4 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.
5.4. 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 all prime-order group operations
and proof-specific operations ("GenerateProof()" and
"VerifyProof()").
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5.5. 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 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 5.2.3.
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.
6. Additive blinding
Let "H" refer to the function "GG.HashToGroup", in Section 2.1 we
assume that the client-side blinding is carried out directly on the
output of "H(x)", i.e. computing "r * H(x)" for some "r <-$ GF(p)".
In the [OPAQUE] document, 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 of computing
additive blinding would be to instead compute "H(x) + (r * G)", where
"G" is the fixed generator for the group "GG".
The advantage of additive blinding is that it allows the client to
pre-process tables of blinded scalar multiplications for "G". This
may give it a computational efficiency advantage (due to the fact
that a fixed-base multiplication can be calculated faster than a
variable-base multiplication). Pre-processing also reduces the
amount of computation that needs to be done in the online exchange.
Choosing one of these values "r * G" (where "r" is the scalar value
that is used), then computing "H(x) + (r * G)" is more efficient than
computing "r * H(x)". 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 variants of the algorithms in Section 3.4 for
performing additive blinding below, along with a new algorithm
"Preprocess". The "Preprocess" algorithm can take place offline and
before the rest of the OPRF protocol. The Blind algorithm takes the
preprocessed values as inputs, but the signature of Unblind remains
the same.
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6.1. Preprocess
struct {
Scalar blind;
SerializedElement blindedGenerator;
SerializedElement blindedPublicKey;
} PreprocessedBlind;
Input:
PublicKey pkS
Output:
PrepocessedBlind preproc
def Preprocess(pkS):
PK = GG.Deserialize(pkS)
r = GG.RandomScalar()
blindedGenerator = GG.Serialize(r * GG.Generator())
blindedPublicKey = GG.Serialize(r * PK)
preproc = PrepocessedBlind{
blind: r,
blindedGenerator: blindedGenerator,
blindedPublicKey: blindedPublicKey,
}
return preproc
6.2. Blind
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Input:
ClientInput input
PreprocessedBlind preproc
Output:
Token token
SerializedElement blindedToken
def Blind(input, preproc):
Q = GG.Deserialize(preproc.blindedGenerator) /* Q = r * G */
P = GG.HashToGroup(input)
token = Token{
data: input,
blind: preproc.blindedPublicKey
}
blindedToken = GG.Serialize(P + Q) /* P + r * G */
return (token, blindedToken)
6.3. Unblind
Input:
Token token
Evaluation ev
SerializedElement blindedToken
Output:
SerializedElement unblinded
def Unblind(token, ev, blindedToken):
PKR = GG.Deserialize(token.blind)
Z = GG.Deserialize(ev.element)
N := Z - PKR
unblindedToken = GG.Serialize(N)
return unblindedToken
Let "P = GG.HashToGroup(x)". Notice that Unblind computes:
Z - PKR = k * (P + r * G) - r * pkS
= k * P + k * (r * G) - r * (k * G)
= k * P
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by the commutativity of scalar multiplication in GG. This is the
same output as in the "Unblind" algorithm for multiplicative
blinding.
Note that the verifiable variant of "Unblind" works as above but
includes the step to "VerifyProof", as specified in Section 3.4.4.
6.3.1. Parameter Commitments
For some applications, it may be desirable for server to bind tokens
to certain parameters, e.g., protocol versions, ciphersuites, etc.
To accomplish this, server should use a distinct scalar for each
parameter combination. Upon redemption of a token T from the client,
server can later verify that T was generated using the scalar
associated with the corresponding parameters.
7. Contributors
o Alex Davidson (alex.davidson92@gmail.com)
o Nick Sullivan (nick@cloudflare.com)
o Chris Wood (caw@heapingbits.net)
o Eli-Shaoul Khedouri (eli@intuitionmachines.com)
o Armando Faz Hernandez (armfazh@cloudflare.com)
8. 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.
9. References
9.1. Normative References
[BB04] "Short Signatures Without Random Oracles",
.
[BG04] "The Static Diffie-Hellman Problem",
.
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[ChaumBlindSignature]
"Blind Signatures for Untraceable Payments",
.
[ChaumPedersen]
"Wallet Databases with Observers",
.
[Cheon06] "Security Analysis of the Strong Diffie-Hellman Problem",
.
[DECAF] "Decaf, Eliminating cofactors through point compression",
.
[DGSTV18] "Privacy Pass, Bypassing Internet Challenges Anonymously",
.
[draft-davidson-pp-protocol]
Davidson, A., "Privacy Pass: The Protocol", n.d.,
.
[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-09 (work in progress), June 2020.
[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",
.
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[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,
.
[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, .
[RFC8017] Moriarty, K., Ed., Kaliski, B., Jonsson, J., and A. Rusch,
"PKCS #1: RSA Cryptography Specifications Version 2.2",
RFC 8017, DOI 10.17487/RFC8017, November 2016,
.
[RISTRETTO]
"The ristretto255 Group", .
[SEC1] Standards for Efficient Cryptography Group (SECG), ., "SEC
1: Elliptic Curve Cryptography",
.
[SEC2] Standards for Efficient Cryptography Group (SECG), ., "SEC
2: Recommended Elliptic Curve Domain Parameters",
.
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[SHAKE] "SHA-3 Standard, Permutation-Based Hash and Extendable-
Output Functions", .
[SJKS17] "SPHINX, A Password Store that Perfectly Hides from
Itself", .
[x9.62] ANSI, "Public Key Cryptography for the Financial Services
Industry: the Elliptic Curve Digital Signature Algorithm
(ECDSA)", ANSI X9.62-1998, September 1998.
9.2. URIs
[1] https://tools.ietf.org/html/draft-irtf-cfrg-voprf-04
[2] https://tools.ietf.org/html/draft-irtf-cfrg-voprf-03
[3] https://tools.ietf.org/html/draft-irtf-cfrg-voprf-02
[4] https://tools.ietf.org/html/draft-irtf-cfrg-voprf-01
Authors' Addresses
Alex Davidson
Cloudflare
County Hall
London, SE1 7GP
United Kingdom
Email: alex.davidson92@gmail.com
Nick Sullivan
Cloudflare
101 Townsend St
San Francisco
United States of America
Email: nick@cloudflare.com
Davidson, et al. Expires January 14, 2021 [Page 34]
Internet-Draft OPRFs July 2020
Christopher A. Wood
Cloudflare
101 Townsend St
San Francisco
United States of America
Email: caw@heapingbits.net
Davidson, et al. Expires January 14, 2021 [Page 35]