コード例 #1
0
ファイル: library.py プロジェクト: lance6716/SPDZ-2
def Norm(b, k, f, kappa, simplex_flag=False):
    """
        Computes secret integer values [c] and [v_prime] st.
        2^{k-1} <= c < 2^k and c = b*v_prime
    """
    # For simplex, we can get rid of computing abs(b)
    temp = None
    if simplex_flag == False:
        temp = b.less_than(0, 2 * k)
    elif simplex_flag == True:
        temp = cint(0)

    sign = 1 - 2 * temp # 1 - 2 * [b < 0]
    absolute_val = sign * b

    #next 2 lines actually compute the SufOR for little indian encoding
    bits = absolute_val.bit_decompose(k, kappa)[::-1]
    suffixes = PreOR(bits)[::-1]

    z = [0] * k
    for i in range(k - 1):
        z[i] = suffixes[i] - suffixes[i+1]
    z[k - 1] = suffixes[k-1]

    #doing complicated stuff to compute v = 2^{k-m}
    acc = cint(0)
    for i in range(k):
        acc += two_power(k-i-1) * z[i]

    part_reciprocal = absolute_val * acc
    signed_acc = sign * acc

    return part_reciprocal, signed_acc
コード例 #2
0
ファイル: library.py プロジェクト: lance6716/SPDZ-2
def sint_cint_division(a, b, k, f, kappa):
    """
        type(a) = sint, type(b) = cint
    """
    theta = int(ceil(log(k/3.5) / log(2)))
    two = cint(2) * two_power(f)
    sign_b = cint(1) - 2 * cint(b < 0)
    sign_a = sint(1) - 2 * sint(a < 0)
    absolute_b = b * sign_b
    absolute_a = a * sign_a
    w0 = approximate_reciprocal(absolute_b, k, f, theta)

    A = Array(theta, sint)
    B = Array(theta, cint)
    W = Array(theta, cint)

    A[0] = absolute_a
    B[0] = absolute_b
    W[0] = w0


    @for_range(1, theta)
    def block(i):
        A[i] = TruncPr(A[i - 1] * W[i - 1], 2*k, f, kappa)
        temp = shift_two(B[i - 1] * W[i - 1], f)
        # no reading and writing to the same variable in a for loop.
        W[i] = two - temp
        B[i] = temp
    return (sign_a * sign_b) * A[theta - 1]
コード例 #3
0
ファイル: library.py プロジェクト: lance6716/SPDZ-2
def cint_cint_division(a, b, k, f):
    """
        Goldschmidt method implemented with
        SE aproximation:
        http://stackoverflow.com/questions/2661541/picking-good-first-estimates-for-goldschmidt-division
    """
    # theta can be replaced with something smaller
    # for safety we assume that is the same theta from previous GS method

    theta = int(ceil(log(k/3.5) / log(2)))
    two = cint(2) * two_power(f)

    sign_b = cint(1) - 2 * cint(b < 0)
    sign_a = cint(1) - 2 * cint(a < 0)
    absolute_b = b * sign_b
    absolute_a = a * sign_a
    w0 = approximate_reciprocal(absolute_b, k, f, theta)
    A = Array(theta, cint)
    B = Array(theta, cint)
    W = Array(theta, cint)

    A[0] = absolute_a
    B[0] = absolute_b
    W[0] = w0
    for i in range(1, theta):
        A[i] = shift_two(A[i - 1] * W[i - 1], f)
        B[i] = shift_two(B[i - 1] * W[i - 1], f)
        W[i] = two - B[i]

    return (sign_a * sign_b) * A[theta - 1]
コード例 #4
0
ファイル: library.py プロジェクト: rdragos/SPDZ-Yao
def approximate_reciprocal(divisor, k, f, theta):
    """
        returns aproximation of 1/divisor
        where type(divisor) = cint
    """
    def twos_complement(x):
        bits = x.bit_decompose(k)[::-1]
        bit_array = Array(k, cint)
        bit_array.assign(bits)

        twos_result = MemValue(cint(0))

        @for_range(k)
        def block(i):
            val = twos_result.read()
            val <<= 1
            val += 1 - bit_array[i]
            twos_result.write(val)

        return twos_result.read() + 1

    bit_array = Array(k, cint)
    bits = divisor.bit_decompose(k)[::-1]
    bit_array.assign(bits)

    cnt_leading_zeros = MemValue(regint(0))

    flag = MemValue(regint(0))
    cnt_leading_zeros = MemValue(regint(0))
    normalized_divisor = MemValue(divisor)

    @for_range(k)
    def block(i):
        flag.write(flag.read() | bit_array[i] == 1)

        @if_(flag.read() == 0)
        def block():
            cnt_leading_zeros.write(cnt_leading_zeros.read() + 1)
            normalized_divisor.write(normalized_divisor << 1)

    q = MemValue(two_power(k))
    e = MemValue(twos_complement(normalized_divisor.read()))

    qr = q.read()
    er = e.read()

    for i in range(theta):
        qr = qr + shift_two(qr * er, k)
        er = shift_two(er * er, k)

    q = qr
    res = shift_two(q, (2 * k - 2 * f - cnt_leading_zeros))

    return res
コード例 #5
0
ファイル: library.py プロジェクト: lance6716/SPDZ-2
def approximate_reciprocal(divisor, k, f, theta):
    """
        returns aproximation of 1/divisor
        where type(divisor) = cint
    """
    def twos_complement(x):
        bits = x.bit_decompose(k)[::-1]
        bit_array = Array(k, cint)
        bit_array.assign(bits)

        twos_result = MemValue(cint(0))
        @for_range(k)
        def block(i):
            val = twos_result.read()
            val <<= 1
            val += 1 - bit_array[i]
            twos_result.write(val)

        return twos_result.read() + 1

    bit_array = Array(k, cint)
    bits = divisor.bit_decompose(k)[::-1]
    bit_array.assign(bits)

    cnt_leading_zeros = MemValue(regint(0))

    flag = MemValue(regint(0))
    cnt_leading_zeros = MemValue(regint(0))
    normalized_divisor = MemValue(divisor)

    @for_range(k)
    def block(i):
        flag.write(flag.read() | bit_array[i] == 1)
        @if_(flag.read() == 0)
        def block():
            cnt_leading_zeros.write(cnt_leading_zeros.read() + 1)
            normalized_divisor.write(normalized_divisor << 1)

    q = MemValue(two_power(k))
    e = MemValue(twos_complement(normalized_divisor.read()))

    qr = q.read()
    er = e.read()

    for i in range(theta):
        qr = qr + shift_two(qr * er, k)
        er = shift_two(er * er, k)

    q = qr
    res = shift_two(q, (2*k - 2*f - cnt_leading_zeros))

    return res
コード例 #6
0
ファイル: library.py プロジェクト: ryandeng1/mc2-copy
def FPDiv(a, b, k, f, kappa, simplex_flag=False):
    """
        Goldschmidt method as presented in Catrina10,
    """
    theta = int(ceil(log(k/3.5) / log(2)))
    alpha = two_power(2*f)
    w = AppRcr(b, k, f, kappa, simplex_flag)
    x = alpha - b * w

    y = a * w
    y = TruncPr(y, 2*k, f, kappa)

    for i in range(theta):
        y = y * (alpha + x)
        x = x * x
        y = TruncPr(y, 2*k, 2*f, kappa)
        x = TruncPr(x, 2*k, 2*f, kappa)

    y = y * (alpha + x)
    y = TruncPr(y, 2*k, 2*f, kappa)
    return y