kTimesG
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July 23, 2024, 02:32:42 PM
Last edit: July 23, 2024, 02:46:09 PM by kTimesG Merited by albert0bsd (2) |
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You can use nomachine's Rust Kangaroo a few posts back, or write a Python script that does the job according to your requirements (e.g. "that actually works").
Too slow? Well, don't expect to find something that "actually works", is fast, and solves 130, sitting out there for you to snug up and inherit 13 BTC tomorrow.
Not in Rust. Pure C++ with GMP. Here is the latest version that goes 470K Hops per second. Theoretically, with 12 cores, it can achieve 5 million hops per second. The more cores you have, the better the result will be. However, it's not worth using this for a puzzle above 70bit. A GPU must be used instead.... Pure self-contained Python Kangaroo with no libraries required. 225K Hops per second. DO NOT USE THIS TO SEARCH FOR 130. It is just an educational, reference-only example. All the math uses Python integers. kangaroo.py import math, os, sys, time
class S: # Scalar field
N = 0xfffffffffffffffffffffffffffffffebaaedce6af48a03bbfd25e8cd0364141
@staticmethod
def add(a, b):
return (a + b) % S.N
class F: # Curve field
P = 0xfffffffffffffffffffffffffffffffffffffffffffffffffffffffefffffc2f
@staticmethod
def add(a, b):
return (a + b) % F.P
@staticmethod
def mul(a, b):
return (a * b) % F.P
@staticmethod
def pow(b, e):
return pow(b, e, F.P)
@staticmethod
def sqrt(a):
return F.pow(a, (F.P + 1) // 4)
@staticmethod
def inv(a):
if a == 0:
return 0
r = 1
s = 0
low = a % F.P
high = F.P
while low > 1:
q = high // low
nm = s - q * r
nw = high - low * q
high = low
s = r
low = nw
r = nm
return r % F.P
class Point: # Affine point
def __init__(self, x, y, parity=-1):
self.x = x
self.y = y if parity == -1 else Point.calc_y(x, parity)
@classmethod
def uncompress(cls, s):
parity, xh = int(s[:2], 16), s[2:]
if parity not in [2, 3]:
raise Exception("Expected parity 02 or 03")
return Point(int(xh, 16), 0, parity % 2)
@staticmethod
def calc_y(x, parity):
y = F.sqrt(F.add(F.pow(x, 3), 7)) # y = sqrt(x**3 + 7)
return y if parity == y % 2 else F.P - y
class JPoint: # Jacobian point
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
def affine(self):
z = F.inv(self.z)
z2 = F.mul(z, z)
return Point(F.mul(self.x, z2), F.mul(self.y, F.mul(z, z2)))
def mul(self, n):
if self.y == 0 or n == 0:
return JPoint(0, 0, 1)
if n == 1:
return self
if n < 0 or n >= S.N:
return self.mul(n % S.N)
if (n % 2) == 0:
return self.mul(n // 2).double()
return self.mul(n // 2).double().add(self)
def double(self):
if self.y == 0:
return JPoint(0, 0, 0)
y2 = F.mul(self.y, self.y)
s = F.mul(4 * self.x, y2)
M = F.mul(3 * self.x, self.x)
x = F.add(F.mul(M, M), - 2 * s)
return JPoint(x, F.add(F.mul(M, s - x), -F.mul(8 * y2, y2)), F.mul(2 * self.y, self.z))
def add(self, q):
if self.y == 0:
return q
if q.y == 0:
return self
qz2 = F.mul(q.z, q.z)
pz2 = F.mul(self.z, self.z)
U1 = F.mul(self.x, qz2)
U2 = F.mul(q.x, pz2)
S1 = F.mul(self.y, F.mul(q.z, qz2))
S2 = F.mul(q.y, F.mul(self.z, pz2))
if U1 == U2:
if S1 != S2:
return JPoint(0, 0, 1)
return self.double()
H = F.add(U2, -U1)
R = F.add(S2, -S1)
H2 = F.mul(H, H)
H3 = F.mul(H, H2)
U1H2 = F.mul(U1, H2)
nx = F.add(F.mul(R, R), -F.add(H3, 2 * U1H2))
ny = F.add(F.mul(R, F.add(U1H2, -nx)), -F.mul(S1, H3))
nz = F.mul(H * self.z, q.z)
return JPoint(nx, ny, nz)
class Group:
G = Point(
0x79be667ef9dcbbac55a06295ce870b07029bfcdb2dce28d959f2815b16f81798,
0x483ada7726a3c4655da4fbfc0e1108a8fd17b448a68554199c47d08ffb10d4b8
)
@staticmethod
def add(p, q):
m = F.mul(F.add(p.y, -q.y), F.inv(F.add(p.x, -q.x)))
x = F.add(F.add(F.mul(m, m), -p.x), -q.x)
return Point(x, F.add(F.mul(m, F.add(q.x, -x)), -q.y))
@classmethod
def mul(cls, p, k):
# [k]P point scalar multiplication
return JPoint(p.x, p.y, 1).mul(k).affine()
@classmethod
def batch_add(cls, ga, gb):
n = len(ga)
d = [0] * n
p = [0] * n
z = 1
for i in range(n):
d[i] = F.add(ga[i].x, -gb[i].x)
z = F.mul(z, d[i])
p[i] = z
t = F.inv(z)
for i in range(n - 1, -1, -1):
if i > 0:
xi = F.mul(t, p[i - 1])
t = F.mul(t, d[i])
else:
xi = t
m = F.mul(F.add(ga[i].y, -gb[i].y), xi)
ga[i].x = F.add(F.add(F.mul(m, m), -ga[i].x), -gb[i].x)
ga[i].y = F.add(F.mul(m, F.add(gb[i].x, -ga[i].x)), -gb[i].y)
class TrueRandom:
def __init__(self, max_value: int, num_bits: int, min_value: int = 0):
self.upper_bound = max_value - min_value + 1
self.min = min_value
self.num_bits = num_bits
self.domain = 2 ** num_bits
self.num_bytes = math.ceil(num_bits / 8)
self.shift = 8 * self.num_bytes - num_bits
def get_next(self):
random_bytes = os.urandom(self.num_bytes)
# trim to num_bits
random_int = int.from_bytes(random_bytes, byteorder='big') >> self.shift
# normalize from domain range to target range
sample = self.upper_bound * random_int // self.domain
return sample + self.min
def kangaroo_with_results(k1, k2, P, dp, herd_size):
k_cand, counter, tbl_size = kangaroo(k1, k2, P, dp, herd_size=herd_size)
k = k_cand[0]
if k1 <= k <= k2:
P_check = Group.mul(Group.G, k)
if P_check.x == P.x:
print(f'Key: {hex(k)}\nGroup ops: {counter}')
return k_cand, counter, tbl_size
def create_kangaroo(kang_type, pos: int, herd_pts, herd_distances, k1, k2, P, rnd: TrueRandom, v):
if kang_type == 0:
# d = rnd.get_next() # [0, k2 - k1)
d = (k2 - k1 + 1) + pos * v # b/2 + i*v
# d = (k2 - k1 + 1) // 2
herd_distances[pos] = d
herd_pts[pos] = Group.mul(Group.G, k1 + d)
else:
# d = rnd.get_next() - (k2 - k1 + 1) // 2 # [-(k2-k1)/2, (k2-k1)/2]
d = (k2 - k1 + 1) // 2 + pos * v # b + i*v
# d = 0
herd_distances[pos] = d
herd_pts[pos] = Group.add(P, Group.mul(Group.G, d))
def check_col(kang_type, hashmap, herd, dist, pos, k1, k2, P, dp_mask, R, v_rnd,
respawn_dead=True,
stop_at_dp=True):
x = herd[pos].x
if x & dp_mask == 0:
item = hashmap.get(x)
if item is not None:
if item[0] == kang_type ^ 1:
# collision
d_wild, d_tame = (item[1], dist[pos]) if kang_type == 0 else (dist[pos], item[1])
return [S.add(k1 + d_tame, - d_wild)], 0
else:
# print(f'Dead kangaroo at {pos}')
if respawn_dead:
# create_kangaroo(kang_type, pos, herd, dist, k1, k2, P, R)
# move along with a small random value
d = v_rnd.get_next()
dist[pos] += d
herd[pos] = Group.add(herd[pos], Group.mul(Group.G, d))
# this will recurse until a non-dead kangaroo is produced
k, created = check_col(kang_type, hashmap, herd, dist, pos, k1, k2, P, dp_mask, R, v_rnd)
return k, 1 + created
else:
hashmap[x] = (kang_type, dist[pos])
return 0, 0
def build_jump_distances(alpha, with_points=False):
jump_points = []
jump_distances = []
# compute A (jump distances) such that average(A) is closest to expected alpha
# Pollard says choosing A as powers of two feels like "needs more investigation"
min_diff = 1
while True:
jump_distance = 2 ** len(jump_distances)
jump_distances.append(jump_distance)
if with_points:
jump_points.append(Group.mul(Group.G, jump_distance))
alpha_real = sum(jump_distances) / len(jump_distances)
diff = abs(1 - alpha_real / alpha)
if alpha_real >= alpha:
if diff > min_diff:
jump_distances.pop()
break
if diff < min_diff:
min_diff = diff
return jump_distances, jump_points
def kangaroo(k1, k2, P, dp: int, herd_size: int = 128):
b = k2 - k1 + 1 # size of search interval
m = herd_size + herd_size # m/2 + m/2
# parallel case - minimize alpha for total number of jumps
alpha = m * math.sqrt(b) / 4 # m * sqrt(b) / 4
jump_distances, jump_points = build_jump_distances(alpha, with_points=True)
alpha_real = sum(jump_distances) / len(jump_distances)
n = len(jump_distances)
# adjust alpha to the actual average jump distance
alpha_expected = alpha
alpha = alpha_real
# expected total number of jumps for each trailing kangaroo (1 per processor)
expected_trailing_jumps = 2 * math.sqrt(b) / m # 2 * sqrt(b) / m
# beta = 0.553 # serial case
# ab_jumps = int(alpha * beta) # serial case
# max_tame_distance = int(alpha * alpha * beta + b // 2)
# expected number of jumps done by a trailing kangaroo after it enters the [b, ...] region
# this would equal the number of jumps done by a tame kangaroo that starts from b
num_tame_jumps = 4 * alpha / (m * m) # 4 * alpha / (m**2)
max_tame_distance = int(alpha * num_tame_jumps + b) # average jump size * num jumps + start
# max_wild_distance = int(alpha * num_tame_jumps + b/2) # average jump size * num jumps + start
# = (2*a/m)**2 = (sqrt(b) / 2)**2
# set v to the "partition" size for a processor, and not a power of two
v = b // m - 1 # (b/2) / (m/2)
# v = herd_size
v_rnd = TrueRandom(v, 256)
hashmap = {}
wilds: list = [None] * herd_size
tames: list = [None] * herd_size
w_dist = [0] * herd_size
t_dist = [0] * herd_size
counter = 0
done_ab_jumps = 0
expected_total_jumps = math.ceil((num_tame_jumps + expected_trailing_jumps) * herd_size)
print(
f'processors: {m}'
f'\n num jump distances: {n}'
f'\nmax jumps per tame kangaroo: {math.ceil(num_tame_jumps)}'
f'\nmax jumps per wild kangaroo: {math.ceil(expected_trailing_jumps)}'
f'\n expected total jumps: {expected_total_jumps} {math.log2(expected_total_jumps):.2f} bits'
f'\n avg real jump distance: {round(alpha_real)} {math.log2(alpha_real):.2f} bits'
f'\n avg expected jump distance: {round(alpha_expected)} {math.log2(alpha_expected):.2f} bits'
f'\n expected max tame distance: {max_tame_distance} {math.log2(max_tame_distance):.2f} bits'
# f'\n expected max wild distance: {max_wild_distance} {math.log2(max_wild_distance):.2f} bits'
)
R = TrueRandom(k2 - k1, 256, 0)
dp_mask = (1 << dp) - 1
for idx in range(herd_size):
create_kangaroo(0, idx, tames, t_dist, k1, k2, P, R, v)
counter += 1
k, born = check_col(0, hashmap, tames, t_dist, idx, k1, k2, P, dp_mask, R, v_rnd)
counter += born
if k:
return k, counter, len(hashmap)
create_kangaroo(1, idx, wilds, w_dist, k1, k2, P, R, v)
counter += 1
k, born = check_col(1, hashmap, wilds, w_dist, idx, k1, k2, P, dp_mask, R, v_rnd)
counter += born
if k:
return k, counter, len(hashmap)
batch_jp: list = [None] * herd_size
start_time = time.time()
last_p_time = 0
while True:
if done_ab_jumps < num_tame_jumps:
# jump tames
for idx in range(herd_size):
d = tames[idx].x % n
# tames[idx] = Group.add(tames[idx], jump_points[d]) # un-batched addition
batch_jp[idx] = jump_points[d]
t_dist[idx] += jump_distances[d]
Group.batch_add(tames, batch_jp)
for idx in range(herd_size):
counter += 1
k, born = check_col(0, hashmap, tames, t_dist, idx, k1, k2, P, dp_mask, R, v_rnd)
counter += born
if k:
return k, counter, len(hashmap)
done_ab_jumps += 1
if done_ab_jumps >= num_tame_jumps:
# new_max = max(t_dist)
# avg_dist = sum(t_dist) / len(t_dist)
# print(
# f'Tames are done.'
# f'\nExpected max tame distance: {max_tame_distance} {math.log2(max_tame_distance):.2f} bits'
# f'\nAverage max tame distance: {avg_dist} {math.log2(avg_dist):.2f} bits'
# f'\nActual max tame distance: {new_max} {math.log2(new_max):.2f} bits'
# )
# max_tame_distance = max(max_tame_distance, new_max)
# max_wild_distance = int(max_tame_distance + b / 2) # add initial tame - wild gap
# create new tames herd
for idx in range(herd_size):
d = b + idx * v + v_rnd.get_next() # b/2 + i*v + z
t_dist[idx] = d
tames[idx] = Group.mul(Group.G, k1 + d)
counter += 1
done_ab_jumps = 0
for idx in range(herd_size):
d = wilds[idx].x % n
# wilds[idx] = Group.add(wilds[idx], jump_points[d]) # unbatched addition
batch_jp[idx] = jump_points[d]
w_dist[idx] += jump_distances[d]
Group.batch_add(wilds, batch_jp)
for idx in range(herd_size):
counter += 1
k, born = check_col(1, hashmap, wilds, w_dist, idx, k1, k2, P, dp_mask, R, v_rnd)
counter += born
if k:
return k, counter, len(hashmap)
if w_dist[idx] > max_tame_distance:
# create_kangaroo(1, idx, wilds, w_dist, k1, k2, P, R)
z = v_rnd.get_next()
d = b // 2 + idx * v + z # b + i*v + z
w_dist[idx] = d
wilds[idx] = Group.add(P, Group.mul(Group.G, d))
counter += 1
total_time = time.time() - start_time
if total_time - last_p_time > 3:
last_p_time = total_time
print(f'Ops: {counter} Table size: {len(hashmap)} Speed: {counter / total_time:.0f} ops/s')
def run_puzzle(idx: int, pub_key, dp: int = 0, herd_size: int = 128, benchmark=0):
# puzzle #X has (X - 1) unknown bits
k1 = 1 << (idx - 1)
k2 = (k1 << 1) - 1
# subtract k1 to search in a [0, k2 - k1) interval
k2 -= k1
k1 = 0
P = Point.uncompress(pub_key)
# subtract (k2 - k1)G from P to bring target point's k to [0, k2 - k1) interval
P = Group.add(P, Group.mul(Group.G, -(k2 + 1)))
now = time.time()
_, ops, hashmap_size = kangaroo_with_results(k1, k2, P, dp, herd_size)
total_time = time.time() - now
print(f'Ops: {ops} Stored: {hashmap_size}')
print(f'Speed: {ops / total_time:.0f} ops/s')
if __name__ == '__main__':
# r, p = int(sys.argv[1]), sys.argv[2]
# run_puzzle(r, p, dp=0, herd_size=128)
# run_puzzle(32, '0209c58240e50e3ba3f833c82655e8725c037a2294e14cf5d73a5df8d56159de69',
# herd_size=512)
run_puzzle(48, '0291bee5cf4b14c291c650732faa166040e4c18a14731f9a930c1e87d3ec12debb',
dp=8, herd_size=1024)
Puzzle 48: processors: 2048
num jump distances: 38
max jumps per tame kangaroo: 6899
max jumps per wild kangaroo: 11586
expected total jumps: 18927375 24.17 bits
avg real jump distance: 7233629130 32.75 bits
avg expected jump distance: 6074001000 32.50 bits
expected max tame distance: 190638869267657 47.44 bits
...
Ops: 17607859 Table size: 68719 Speed: 225400 ops/s
Key: 0x2de6d7ce3b9b
Group ops: 18288933
Ops: 18288933 Stored: 71317
Speed: 222616 ops/s
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