diff options
Diffstat (limited to 'util/utils.py')
| -rw-r--r-- | util/utils.py | 60 |
1 files changed, 41 insertions, 19 deletions
diff --git a/util/utils.py b/util/utils.py index 1c620ef..014c4c6 100644 --- a/util/utils.py +++ b/util/utils.py @@ -1,7 +1,9 @@ +import hashlib import numpy as np from matplotlib import ticker from math import sqrt, log - +import ec +from asn1crypto.core import Sequence def hw(i): res = 0 @@ -18,19 +20,55 @@ def moving_average(a, n) : def time_scale(data, orig_unit, target_unit, scaling_factor): + if orig_unit == "instr": + return orig_unit units = { "milli": ("ms", 1000000), "micro": (r"$\mu s$", 1000), "nano": ("ns", 1) } - upper = units[orig_unit][1] - lower = units[target_unit][1] * scaling_factor + upper = units[orig_unit][1] * scaling_factor + lower = units[target_unit][1] if upper > lower: data *= upper // lower elif lower > upper: np.floor_divide(data, lower // upper, data) return (r"$\frac{1}{" + str(scaling_factor) + "}$" if scaling_factor != 1 else "") + units[target_unit][0] +def recompute_nonces(data, curve_name, hash_algo): + try: + curve = ec.get_curve(curve_name) + except: + curve = ec.load_curve(curve_name) + verified = False + for elem in data: + if elem["nonce"] is not None: + continue + if elem["index"] % (len(data)//10) == 0: + print(".", end="") + if hash_algo is None: + hm = int.from_bytes(elem["data"], byteorder="big") % curve.group.n + else: + h = hashlib.new(hash_algo, elem["data"]) + hm = int(h.hexdigest(), 16) + if h.digest_size * 8 > curve.group.n.bit_length(): + hm >> h.digest_size * 8 - curve.group.n.bit_length() + hm = hm % curve.group.n + r, s = Sequence.load(elem["signature"]).native.values() + r = ec.Mod(r, curve.group.n) + s = ec.Mod(s, curve.group.n) + rx = r * elem["priv"] + hmrx = hm + rx + nonce = s.inverse() * hmrx + if not verified: + res = int(nonce) * curve.g + if int(res.x) % curve.group.n != int(r): + print("Nonce recomputation couldnt verify!") + raise ValueError + else: + print("Nonce recomputation works") + verified = True + elem["nonce"] = int(nonce) def hist_size_func(choice): if choice == "sqrt": @@ -71,19 +109,3 @@ def miller_correction(entropy, samples, bins): return entropy + (bins - 1)/(2*samples) -def egcd(a, b): - if a == 0: - return b, 0, 1 - else: - g, y, x = egcd(b % a, a) - return g, x - (b // a) * y, y - - -def mod_inv(a, p): - if a < 0: - return p - mod_inv(-a, p) - g, x, y = egcd(a, p) - if g != 1: - raise ArithmeticError("Modular inverse does not exist") - else: - return x % p |