Network Working Group W. Ladd
Internet-Draft UC Berkeley
Intended status: Standards Track P. Longa
Expires: March 26, 2017 Microsoft Research
R. Barnes
September 22, 2016



This document specifies a twisted Edwards curve that takes advantage of arithmetic over the field GF(2^127-1) and two endomorphisms to achieve the speediest Diffie-Hellman key agreements over a group of order approximately 2^246, which provides around 128 bits of security. Curve4Q implementations are more than two times faster than those of Curve25519 and, when not using endomorphisms, are between 1.2 and 1.6 times faster.

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Table of Contents

1. Introduction

Public key cryptography continues to be computationally expensive, particularly on less powerful devices. While recent advances in efficient formulas for addition and doubling have substantially reduced the cost of elliptic curve operations in terms of field operations, the number of group operations involved in scalar multiplication has not been reduced in the curves considered for IETF use. Using curves with efficiently computable endomorphisms can reduce the number of group operations by turning one long scalar multiplication into the sum of several multiplications by smaller scalars, which can be evaluated more efficiently.

For curves over quadratic extension fieldss, there are more endomorphism families to choose from, and the field operations are often more efficient compared to prime fields of the same size. The ideal case is given by curves equipped with two distinct endomorphisms, so that it becomes possible to divide scalars into four parts. We focus on curves defined over the field GF(p^2) for the Mersenne prime p = 2^127 - 1, which offers extremely efficient arithmetic. Together, these improvements substantially reduce computation time compared to other proposed Diffie-Hellman key exchange and digital signature schemes. However, the combined availability of these features severely restricts the curves that can be used for cryptographic applications.

As described in [Curve4Q], the elliptic curve “Curve4Q” defined in this document is the only known elliptic curve that (1) permits a four dimensional decomposition (using two endomorphisms) over GF(p^2) and (2) has a large prime order subgroup. The order of this subgroup is approximately 2^246, which provides around 128 bits of security. No other known elliptic curve with such a decomposition has a larger prime order subgroup over this field. This “uniqueness” allays concerns about selecting curves vulnerable to undisclosed attacks.

Curve4Q can be used to implement Diffie-Hellman key exchange, as described below. It is also possible to use Curve4Q as the basis for digital signature scheme (e.g., [SchnorrQ]).

2. Mathematical Prerequisites

Curve4Q is defined over the finite field GF(p^2), where p is the Mersenne prime 2^127 - 1. Elements of this finite field have the form (a + b * i), where a and b are elements of the finite field GF(p) (i.e., integers mod p) and i^2 = -1.

Let A = a0 + a1*i and B = b0 + b1*i be two elements of GF(p^2). Below we present formulas for computing addition, subtraction, multiplication, squaring, conjugation and inversion.

A + B = (a0 + b0) + (a1 + b1)*i

A - B = (a0 - b0) + (a1 - b1)*i

A * B = (a0*b0 - a1*b1) + ((a0 + a1)*(b0 + b1)-(a0*b0 - a1*b1))*i
      = (a0*b0 - a1*b1) + (a0*b1 + a1*b0)*i

A * A = (a0 + a1)*(a0 - a1) + 2*a0*a1*i

conj(A) = a0 - a1*i

1/A = conj(A) / (a0^2 + a1^2)

The GF(p) division in the formula for 1/A can be computed using an exponentiation via Fermat’s little theorem: 1/a = a^(p - 2) = a^(2^127 - 3) for any element a of GF(p). One can use a fixed addition chain to compute a^(2^127 - 3) (e.g., see [FourQlib]).

Curve4Q is the twisted Edwards curve E over GF(p^2) defined by the following curve equation:

E: -x^2 + y^2 = 1 + d * x^2 * y^2, with

d = 0x00000000000000e40000000000000142 +
    0x5e472f846657e0fcb3821488f1fc0c8d * i

Let E(GF(p^2)) be the set of pairs (x, y) of elements of GF(p^2) satisfying this equation. This set forms a group with the addition operation (x1, y1) + (x2, y2) = (x3, y3), where:

          x1 * y2 + y1 * x2                y1 * y2 + x1 * x2
x3 = ---------------------------, y3 = ---------------------------
      1 + d * x1 * y1 * x2 * y2         1 - d * x1 * y1 * x2 * y2

As d is not a square in GF(p^2), and -1 is, this formula never involves a division by zero when applied to points on the curve. That is, the formula is complete and works without exceptions for any input in E(GF(p^2)). The identity element is (0, 1), and the inverse of (x, y) is (-x, y). The order of this group is #E = 2^3 · 7^2 · N, where N is the following 246-bit prime:

N = 0x29cbc14e5e0a72f05397829cbc14e5dfbd004dfe0f79992fb2540ec7768ce7

Points P on E such that [N]*P = (0, 1) are N-torsion points. Given a point P and Q which are both N-torsion points, it is difficult to find m such that Q = [m]*P. This is the elliptic curve discrete logarithm problem, which is closely related to the security of Diffie-Hellman key exchanges as the best known attacks on the Diffie-Hellman problem involve solving the discrete logarithm problem. The best known algorithms take approximately 2^123 group operations.

This group has two different efficiently computable endomorphisms, as described in [Curve4Q]. As discussed in [GLV] and [GLS], these endomorphisms allow a multiplication by a large scalar to be computed using multiple multiplications by smaller scalars, which can be evaluated in much less time overall.

3. Representation of Curve Points

Elements a in GF(p) are represented as 16 byte little endian integers which are the numbers in the range [0, p). The 16 bytes b[0], b[1], … b[15] represent b[0] + 256*b[1] + 256^2*b[2] + … + 256^15*b[15]. Since we are representing numbers in the range [0, 2^127-1), the top bit of b[15] is always zero.

An element x0 + x1*i of GF(p^2) is represented on the wire by the concatenation of the encodings for x0 and x1. A point (x, y) on Curve4Q is serialized in a compressed form as the representation of y with a modified top bit. This top bit is used to disambiguate between x and -x during decoding.

To carry out this disambiguation we use the lexicographic order of elements in GF(p^2): define two elements a = a0 + a1*i and b = b0 + b1*i with all their coordinates in [0, p); a is greater than b if a0 is greater than b0. If a0 and b0 are equal, a is greater than b if a1 is greater than b1.

Set the coordinate value x and its negative -x. The top bit of a compressed point is 0 if x is smaller than -x. Otherwise, the top bit is 1.

|--------------- y ---------------|
|       y0     |0|       y1     |s|

To decode an encoded point from a 32-byte sequence B:

The appendix Appendix B details an algorithm for decoding a point following the steps above.

We call the operation of compressing a point P into 32 bytes Compress(P), and decompression Expand(S). Expand(Compress(P))=P for all the points P on the curve, and Compress(Expand(S))=S if and only if S is a valid representation of a point.

Not all 32 byte strings represent valid points. Implementations MUST reject invalid strings and check that decompression is successful. Strings are invalid if they are not possible outputs of the compression operator. In particular the values of y0 and y1 MUST be less then p.

4. Scalar multiplication

Below, we present two algorithms for scalar multiplication on Curve4Q. Each algorithm takes as input a 256-bit unsigned integer m and an N-torsion point P and computes the product [m]*P.

The first algorithm uses a simple fixed-window exponentiation without exploiting endomorphisms. The second algorithm uses endomorphisms to accelerate computation. The execution of operations in both algorithms has a regular pattern in order to enable constant-time implementations and protect against timing and simple side channel attacks. Both algorithms use the same addition and doubling formulas.

First, we discuss explicit formulas and efficient projective coordinate representations.

4.1. Alternative Point Representations and Addition Laws

We use coordinates based on extended twisted Edwards coordinates introduced in [TwistedRevisited]: the tuple (X, Y, Z, T) with Z nonzero and Z * T = X * Y corresponds to a point (x, y) satisfying x = X/Z and y = Y/Z. The neutral point in this representation is (0, 1, 1, 0). The following slight variants are used in the optimized scalar multiplication algorithm in order to save computations: point representation R1 is given by (X, Y, Z, Ta, Tb), where T=Ta * Tb; representation R2 is (N, D, E, F) = (X + Y, Y- X, 2Z, 2dT); representation R3 is (N, D, Z, T) = (X + Y, Y - X, Z, T); and representation R4 is (X, Y, Z). Similar “caching” techniques were discussed in [TwistedRevisited] to accelerate repeated additions of the same point. Converting between these representations is straightforward.

A point doubling (DBL) takes an R4 point and produces an R1 point. For addition, we first define an operation ADD_core that takes an R2 and an R3 point and produces an R1 point. This can be used to implement an operation ADD which takes an R1 and an R2 point as inputs (and produces an R1 point) by first converting the R1 point to R3, and then executing ADD_core. Exposing these operations and the multiple representations helps save time by avoiding redundant computations: the conversion of the first argument to ADD can be done once if the argument will be used in multiple additions.

Below, we list the explicit formulas for the required point operations. These formulas, which are adapted from [Twisted] and [TwistedRevisited], are complete: they have no exceptional cases, and therefore can be used in any algorithm for computing scalar multiples without worrying about exceptional procedure attacks [Exceptional]. Note that we do not explicitly note the point format every time an addition or doubling is used, and assume that conversions are done when required.

DBL and ADD_core are computed as follows:

DBL(X1, Y1, Z1):
  A = X1^2
  B = Y1^2
  C = 2 * Z1^2
  D = A + B
  E = (X1 + Y1)^2 - D
  F = B - A
  G = C - F
  X3 = E * G
  Y3 = D * F
  Z3 = F * G
  Ta3 = E
  Tb3 = D
return(X3, Y3, Z3, Ta3, Tb3)

ADD\_core(N1, D1, E1, F1, N2, D2, Z2, T2):
   A = D1 * D2
   B = N1 * N2
   C = T2 * F1
   D = Z2 * E1
   E = B - A
   F = D - C
   G = D + C
   H = B + A
   X3 = E * F
   Y3 = G * H
   Z3 = F * G
   Ta3 = E
   Tb3 = H
return (X3, Y3, Z3, Ta3, Tb3)

4.2. Multiplication without Endomorphisms

We begin by taking our input point P, and computing a table of points containing T[0] = [1]P, T[1] = [3]P, … , T[7] = [15]P as follows:

Q = DBL(P)
Convert Q to R2 form
T[0] = P
Convert T[0] to R2 form
for i=1 to 7:
    T[i] = ADD_core(Q, T[i-1])
    Convert T[i] to R2 form

Next, take m and reduce it modulo N. Then, add N if necessary to ensure that m is odd. At this point, we recode m into a signed digit representation consisting of 63 signed, odd digits d[i] in base 16. The following algorithm accomplishes this task.

for i=0 to 61:
    d[i] = (m mod 32) - 16
    m = (m - d[i]) / 16
d[62] = m

Finally, the computation of the multiplication is as follows.

Let ind = (abs(d[62]) - 1) / 2
Let sign = sgn(d[62])
Q = sign * T[ind]
Convert Q into R4 form
for i from 61 to 0:
    Q = DBL(Q)
    Q = DBL(Q)
    Q = DBL(Q)
    Q = DBL(Q)
    ind = (abs(d[i]) - 1) / 2
    sign = sgn(d[i])
    S = sign * T[ind]
    Q = ADD(Q, S)
return Q

As sign is either -1 or 1, the multiplication sign * T[ind] is simply a conditional negation. To negate a point (N, D, E, F) in R2 form one computes (D, N, E, -F). The table lookups and conditional negations must be carefully implemented as described in ``Security Considerations’’ to avoid side-channel attacks. This algorithm MUST NOT be applied to points which are not N-torsion points; it will produce the wrong answer.

4.3. Multiplication with Endomorphisms

This algorithm makes use of the identity [m]*P = [a_1]*P + [a_2]*phi(P) + [a_3]*psi(P) + [a_4]*psi(phi(P)), where a_1, a_2, a_3, and a_4 are 64-bit scalars that depend on m. The overall product can then can be computed using a small table of 8 precomputed points and 64 doublings and additions. This is considerably fewer operations than the number of operations required by the algorithm above, at the cost of a more complicated implementation.

We describe each phase of the computation separately: the computation of the endomorphisms, the scalar decomposition and recoding, the creation of the table of precomputed points and, lastly, the computation of the final results. Each section refers to constants listed in an appendix in order of appearance.

4.3.1. Endomorphisms

The two endomorphisms phi and psi used to accelerate multiplication are computed as phi(Q) = tau_dual(upsilon(tau(Q)) and psi(Q) = tau_dual(chi(tau(Q))). Below, we present procedures for tau, tau_dual, upsilon and chi, adapted from [FourQlib]. Tau_dual produces an R1 point, while the other procedures produce R4 points.

Note: Tau produces points on a different curve, while upsilon and chi are endomorphisms of that different curve. Tau and tau_dual are the isogenies mentioned in the mathematical background above. As a result the intermediate results do not satisfy the equations of the curve E. Implementers who wish to check the correctness of these intermediate results are referred to [Curve4Q].

tau(X1, Y1, Z1):
   A = X1^2
   B = Y1^2
   C = A + B
   D = A - B
   X2 = ctau * X1 * Y1 * D
   Y2 = -(2 * Z1^2 + D) * C
   Z2 = C * D
return(X2, Y2, Z2)
tau_dual(X1, Y1, Z1):
  A = X1^2
  B = Y1^2
  C = A + B
  Ta2 = B - A
  D = 2 * Z1^2 - Ta2
  Tb2 = ctaudual * X1 * Y1
  X2 = C * Tb2
  Y2 = D * Ta2
  Z2 = C * D
return(X2, Y2, Z2, Ta2, Tb2)
upsilon(X1, Y1, Z1):
   A = cphi0 * X1 * Y1
   B = Y1 * Z1
   C = Y1^2
   D = Z1^2
   F = D^2
   G = B^2
   H = C^2
   I = cphi1 * B
   J = C + cphi2 * D
   K = cphi8 * G + H + cphi9 * F
   X2 = conj(A * K * (I + J) * (I - J))
   L = C + cphi4 * D
   M = cphi3 * B
   N = (L + M) * (L - M)
   Y2 = conj(cphi5 * D * N * (H + cphi6 * G + cphi7 * F))
   Z2 = conj(B * K * N)
return(X2, Y2, Z2)
chi(X1, Y1, Z1):
   A = conj(X1)
   B = conj(Y1)
   C = conj(Z1)^2
   D = A^2
   F = B^2
   G = B * (D + cpsi2 * C)
   H = -(D + cpsi4 * C)
   X2 = cpsi1 * A * C * H
   Y2 = G * (D + cpsi3 * C)
   Z2 = G * H
return(X2, Y2, Z2)

4.3.2. Scalar Decomposition and Recoding

This stage has two parts. The first one consists in decomposing the scalar into four 64-bit integers, and the second one consists in recoding these integers into a form that can be used to efficiently and securely compute the scalar multiplication.

The decomposition step uses four fixed vectors called b1, b2, b3, b4, with four 64 bit entries each. In addition, we have integer constants L1, L2, L3, L4, which are used to implement rounding. All these values are listed in Appendix A. In addition, we use two constant vectors derived from these inputs:

Given m, first compute t[i] = floor(L[i] * m / 2^256) for i between 1 and 4. Then compute the vector sum a = (m, 0, 0, 0) - t1 b1 - t2 b2 - t3 b3 - t4 b4. Precisely one of a + c and a + c’ has an odd first coordinate: this is the vector v that is fed into the scalar recoding step. Note that the entries of this vector are 64 bits, so intermediate values in the calculation above can be truncated to this width.

The recoding step takes the vector v=(v1, v2, v3, v4) from the previous step and outputs two arrays m[0]..m[64] and d[0]..d[64]. Each entry of d is between 0 and 7, and each entry in m is -1 or 0. The recoding algorithm is detailed below. bit(x, n) denotes the nth bit of x, counting from least significant to most, starting with 0.

m[64] = -1
for i = 0 to 63 do:
    b1 = bit(v1, i+1)
    d[i] = 0
    m[i] = b1

    for j = 2 to 4 do:
        bj = bit(vj, 0)
        d[i] = d[i] + bj * 2^(j-2)
        c = (b1 or bj) xor b1
        vj = vj / 2 + c
d[64] = v2 + 2 * v3 + 4 * v4

4.3.3. Final Computation

We now describe the last step in the endomorphism based algorithm for computing scalar multiplication. On inputs m and P, the algorithm first precomputes a table of images of P under the endomorphisms, then recodes m, then uses these intermediate artifacts to compute the scalar product.

First, compute a table T of 8 points in representation R2 as shown below. Computations Q = psi(P), R = phi(P) and S = psi(phi(P)) are carried out using formulas from Section 4.3.1.

Q is phi(P) in R3
R is psi(P) in R3
S is psi(Q) in R2
T[0] is P in R2
T[1] is ADD_core(Q, T[0])  # (P + Q)
Convert T[1] to R2
T[2] is ADD_Core(R, T[0])  # (P + R)
Convert T[2] to R2
T[3] is ADD_Core(R, T[1])  # (P + Q + R)
Convert T[3] to R2
T[4] is ADD_Core(S, T[0])  # (P + S)
Convert T[4] to R2
T[5] is ADD_Core(S, T[1])  # (P + Q + S)
Convert T[5] to R2
T[6] is ADD_Core(S, T[2])  # (P + R + S)
Convert T[6] to R2
T[7] is ADD_Core(S, T[3])  # (P + Q + R + S)
Convert T[7] to R2

Second, apply the scalar decomposition and recoding algorithm from Section 4.3.2 to m, to produce the two arrays m[0]..m[64] and d[0]..d[64].

Define s[i] to be 1 if m[i] is 1 and -1 if m[i] is 0. Then the multiplication is completed as follows:

Q = s[64] * T[d[64]]
Convert Q to R4
for i=63 to 0 do:
    Q = DBL(Q)
    Q = ADD(Q, s[i] * T[di])
return Q = (X/Z, Y/Z)

Multiplication by s[i] is simply a conditional negation. To negate an R2 point (N, D, E, F) one computes (D, N, E , -F). It is important to do this (as well as the table lookup) in constant time, i.e., the execution of branches and memory accesses MUST NOT depend on secret values (see ``Security Considerations’’ for more details).

The optimized multiplication algorithm above only works properly for N-torsion points. Implementations MUST NOT use this algorithm on anything that is not known to be an N-torsion point. Otherwise, it will produce the wrong answer, with extremely negative consequences for security.

5. Diffie-Hellman Key Agreement

The above scalar multiplication algorithms can be used to implement Diffie-Hellman with cofactor.

DH(m, P):
      Ensure P on curve and if not return FAILURE

      P1 = DBL(P)                 # [2]P
      P2 = ADD(P1, P)             # [3]P
      P3 = DBL(DBL(DBL(DBL(P2)))) # [48]P
      Q = ADD(P3, P)              # [49]P
      Q = DBL(DBL(DBL(Q))         # [392]P

      Compute [m]*Q

      If Q is the neutral point, return FAILURE
Return [m]*Q in affine coordinates

The role of the separate multiplication by 392 is to ensure that Q is an N-torsion point so that the scalar multiplication algorithms above may be used safely to produce correct results. In other words, as the cofactor is greater than one, Diffie-Hellman computations using Curve4Q MUST always use cofactor clearing (as defined above).

The base point G for Diffie-Hellman operations has the following affine coordinates:

Gx = 0x1A3472237C2FB305286592AD7B3833AA +
Gy = 0x0E3FEE9BA120785AB924A2462BCBB287 +
G = (X, Y)

The tables used in multiplications of this generator (small multiples of G for the multiplication without endomorphisms, or endomorphism images for the optimized multiplication with endomorphisms) can be pre-generated to speed up the first, fixed-point DH computation.

Two users, Alice and Bob, can carry out the following steps to derive a shared key: each picks a random string of 32 bytes, mA and mB, respectively. Alice computes the public key A = Compress(DH(mA, G)), and Bob computes the public key B = Compress(DH(mB, G)). They exchange A and B, and then Alice computes KAB = DH(mA, Expand(B)) while Bob computes KBA = DH(mB, Expand(A)), which produces the shared point K = KAB = KBA. The y coordinate of K, represented as a 32 byte string as detailed in Section 3 is the shared secret.

If the received strings are not valid points, the DH function has failed to compute an answer. Implementations SHOULD return a random 32 byte string as well as return an error, to prevent bugs when applications ignore return codes. They MUST signal an error when decompression fails.

Implementations MAY use any method to carry out these calculations, provided that it agrees with the above function on all inputs and failure cases, and does not leak information about secret keys. For example, refer to the constant-time fixed-base scalar multiplication algorithm implemented in [FourQlib] to accelerate the computation of DH(m, G).

6. IANA Considerations

[RFC Editor: please remove this section prior to publication] This document has no IANA actions.

7. Security Considerations

The best known algorithms for the computation of discrete logarithms on Curve4Q are parallel versions of the Pollard rho algorithm in [Distinguished]. On Curve4Q these attacks take on the order of 2^123 group operations to compute a single discrete logarithm. The additional endomorphisms have large order, and so cannot be used to accelerate generic attacks. Quadratic fields are not affected by any of the index calculus attacks used over larger extension fields.

Implementations MUST check that input points properly decompress to points on the curve. Removing such checks may result in extremely effective attacks. The curve is not twist-secure: implementations using single coordinate ladders MUST validate points before operating on them. In the case of protocols that require contributory behavior, when the identity is the output of the DH primitive it MUST be rejected and failure signaled to higher levels. Notoriously [RFC5246] without [RFC7627] is such a protocol.

Implementations MUST ensure that execution of branches and memory addresses accessed do not depend on secret data. The time variability introduced by secret-dependent operations have been exploited in the past via timing and cache attacks to break implementations. Side-channel analysis is a constantly moving field, and implementers must be extremely careful to ensure that operations do not leak any secret information. Using ephemeral private scalars for each operation (ideally, limiting the use of each private scalar to one single operation) can reduce the impact of side-channel attacks. However, this might not be possible for many applications of Diffie-Hellman key agreement.

In the future quantum computers may render the discrete logarithm problem easy on all abelian groups through Shor’s algorithm. Data intended to remain confidential for significantly extended periods of time SHOULD NOT be protected with any primitive based on the hardness of factoring or the discrete log problem (elliptic curve or finite field).

8. Informative References

[Curve4Q] Costello, C. and P. Longa, "FourQ: four-dimensional decompositions on a Q-curve over the Mersenne prime", 2016.
[Distinguished] van Oorschot, P. and M. Wiener, "Parallel Collision Search with Cryptanalytic Applications", 1996.
[Exceptional] Izu, T. and T. Takagi, "Exceptional procedure attack on elliptic curve cryptosystems", 2003.
[FourQlib] Costello, C. and P. Longa, "FourQlib", 2016.
[GLS] Galbraith, S., Lin, X. and M. Scott, "Endomorphisms for Faster Elliptic Curve Cryptography on a Large Class of Curves", 2009.
[GLV] Gallant, R., Lambert, R. and S. Vanstone, "Faster Point Multiplication on Elliptic Curves with Efficient Endomorphisms", 2001.
[Invsqr] Hamburg, M., "Fast and compact elliptic-curve cryptography", 2012.
[RFC5246] Dierks, T. and E. Rescorla, "The Transport Layer Security (TLS) Protocol Version 1.2", RFC 5246, DOI 10.17487/RFC5246, August 2008.
[RFC7627] Bhargavan, K., Delignat-Lavaud, A., Pironti, A., Langley, A. and M. Ray, "Transport Layer Security (TLS) Session Hash and Extended Master Secret Extension", RFC 7627, DOI 10.17487/RFC7627, September 2015.
[SchnorrQ] Costello, C. and P. Longa, "SchnorrQ: Schnorr Signatures on FourQ", 2016.
[Twisted] Bernstein, D., Birkner, P., Joye, M., Lange, T. and C. Peters, "Twisted Edwards Curves", 2008.
[TwistedRevisited] Hisil, H., Wong, K-H., Carter, G. and E. Dawson, "Twisted Edwards Curves Revisited", 2008.

Appendix A. Constants

ctau = 0x1964de2c3afad20c74dcd57cebce74c3 +
       0x000000000000000c0000000000000012 * i

ctaudual = 0x4aa740eb230586529ecaa6d9decdf034 +
           0x7ffffffffffffff40000000000000011 * i
cphi0 = 0x0000000000000005fffffffffffffff7 +
        0x2553a0759182c3294f65536cef66f81a * i
cphi1 = 0x00000000000000050000000000000007 +
        0x62c8caa0c50c62cf334d90e9e28296f9 * i
cphi2 = 0x000000000000000f0000000000000015 +
        0x78df262b6c9b5c982c2cb7154f1df391 * i
cphi3 = 0x00000000000000020000000000000003 +
        0x5084c6491d76342a92440457a7962ea4 * i
cphi4 = 0x00000000000000030000000000000003 +
        0x12440457a7962ea4a1098c923aec6855 * i
cphi5 = 0x000000000000000a000000000000000f +
        0x459195418a18c59e669b21d3c5052df3 * i
cphi6 = 0x00000000000000120000000000000018 +
        0x0b232a8314318b3ccd3643a78a0a5be7 * i
cphi7 = 0x00000000000000180000000000000023 +
        0x3963bc1c99e2ea1a66c183035f48781a * i
cphi8 = 0x00000000000000aa00000000000000f0 +
        0x1f529f860316cbe544e251582b5d0ef0 * i
cphi9 = 0x00000000000008700000000000000bef +
        0x0fd52e9cfe00375b014d3e48976e2505 * i
cpsi1 = 0x2af99e9a83d54a02edf07f4767e346ef +
        0x00000000000000de000000000000013a * i
cpsi2 = 0x00000000000000e40000000000000143 +
        0x21b8d07b99a81f034c7deb770e03f372 * i
cpsi3 = 0x00000000000000060000000000000009 +
        0x4cb26f161d7d69063a6e6abe75e73a61 * i
cpsi4 = 0x7ffffffffffffff9fffffffffffffff6 +
        0x334d90e9e28296f9c59195418a18c59e * i
L1 = 0x7fc5bb5c5ea2be5dff75682ace6a6bd66259686e09d1a7d4f
L2 = 0x38fd4b04caa6c0f8a2bd235580f468d8dd1ba1d84dd627afb
L3 = 0x0d038bf8d0bffbaf6c42bd6c965dca9029b291a33678c203c
L4 = 0x31b073877a22d841081cbdc3714983d8212e5666b77e7fdc0
b1 = [ 0x0906ff27e0a0a196, -0x1363e862c22a2da0,
       0x07426031ecc8030f, -0x084f739986b9e651]
b2 = [ 0x1d495bea84fcc2d4, -0x0000000000000001,
       0x0000000000000001,  0x25dbc5bc8dd167d0]
b3 = [ 0x17abad1d231f0302,  0x02c4211ae388da51,
      -0x2e4d21c98927c49f,  0x0a9e6f44c02ecd97]
b4 = [ 0x136e340a9108c83f,  0x3122df2dc3e0ff32,
      -0x068a49f02aa8a9b5, -0x18d5087896de0aea]

Appendix B. Point Decompression

The following algorithm is an adaptation of the decompression algorithm from [SchnorrQ]. It decodes a 32-byte string B which is formatted as detailed in Section 3. The result is a valid point P = (x, y) that satisfies the curve equation, or a message of FAILED if the decoding had a failure.

Sign(x0 + x1*i):
    s0 = X[0] >> 126
    s1 = X[1] >> 126
    if X[0] != 0:
        return s0
        return s1

Compress(X, Y):
    B = Y encoded following {{representation-of-curve-points}}
    Set the to bit to Sign(X)
    return B

Expand(B = [y, s]):
    Parse out the encoded values y = y0 + y1 * i and s
    if y0 or y1 >= p:
        return FAILED

    u = y^2 - 1             # Set u = u0 + u1 * i
    v = d*y^2 + 1           # Set v = v0 + v1 * i

    t0 = u0*v0 + u1*v1;
    t1 = u1*v0 - u0*v1;
    t2 = v0^2 + v1^2
    t3 = (t0^2 + t1^2)^(2^125)

    t = 2*(t0 + t3)
    if t = 0:
        t = 2*(t0 - t3)

    a = (t * t2^3)^(2^125-1)
    b = (a * t2) * t
    x0 = b/2
    x1 = (a * t2) * t1
    if t2 * b^2 = t:
        Swap x0 and x1

    x = x0 + x1 * i
    if Sign(x) != s:
      x = -x

    if -x^2+y^2 != 1+d*x^2*y^2:  # Check curve equation with x
        x = conj(x)
    if -x^2+y^2 != 1+d*x^2*y^2:  # ... or its conjugate
        return FAILED
    return P = (x,y)

Authors' Addresses

Watson Ladd UC Berkeley EMail:
Patrick Longa Microsoft Research EMail:
Richard Barnes Mozilla EMail: