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blomer_may.py
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blomer_may.py
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import logging
from sage.all import ZZ
from shared import small_roots
def modular_trivariate(f, N, m, t, X, Y, Z, roots_method="groebner"):
"""
Computes small modular roots of a trivariate polynomial.
More information: Blomer J., May A., "New Partial Key Exposure Attacks on RSA" (Section 4)
:param f: the polynomial
:param N: the modulus
:param m: the parameter m
:param t: the parameter t
:param X: an approximate bound on the x roots
:param Y: an approximate bound on the y roots
:param Z: an approximate bound on the z roots
:param roots_method: the method to use to find roots (default: "groebner")
:return: a generator generating small roots (tuples of x, y, and z roots) of the polynomial
"""
f = f.change_ring(ZZ)
pr = f.parent()
x, y, z = pr.gens()
logging.debug("Generating shifts...")
shifts = []
for i in range(m + 1):
for j in range(i + 1):
for k in range(j + 1):
g = x ** (j - k) * z ** k * N ** i * f ** (m - i)
shifts.append(g)
for k in range(1, t + 1):
h = x ** j * y ** k * N ** i * f ** (m - i)
shifts.append(h)
L, monomials = small_roots.create_lattice(pr, shifts, [X, Y, Z])
L = small_roots.reduce_lattice(L)
polynomials = small_roots.reconstruct_polynomials(L, f, N ** m, monomials, [X, Y, Z])
for roots in small_roots.find_roots(pr, polynomials, method=roots_method):
yield roots[x], roots[y], roots[z]
def modular_bivariate(f, eM, m, t, Y, Z, roots_method="groebner"):
"""
Computes small modular roots of a bivariate polynomial.
More information: Blomer J., May A., "New Partial Key Exposure Attacks on RSA" (Section 6)
:param f: the polynomial
:param eM: the modulus
:param m: the parameter m
:param t: the parameter t
:param Y: an approximate bound on the y roots
:param Z: an approximate bound on the z roots
:param roots_method: the method to use to find roots (default: "groebner")
:return: a generator generating small roots (tuples of y and z roots) of the polynomial
"""
f = f.change_ring(ZZ)
pr = f.parent()
y, z = pr.gens()
logging.debug("Generating shifts...")
shifts = []
for i in range(m + 1):
for j in range(i + 1):
g = y ** j * eM ** i * f ** (m - i)
shifts.append(g)
for j in range(1, t + 1):
h = z ** j * eM ** i * f ** (m - i)
shifts.append(h)
L, monomials = small_roots.create_lattice(pr, shifts, [Y, Z])
L = small_roots.reduce_lattice(L)
polynomials = small_roots.reconstruct_polynomials(L, f, eM ** m, monomials, [Y, Z])
for roots in small_roots.find_roots(pr, polynomials, method=roots_method):
yield roots[y], roots[z]