def test_misc(self):
secint = mpc.SecInt()
secfxp = mpc.SecFxp()
secfld = mpc.SecFld()
for secnum in (secint, secfxp, secfld):
self.assertEqual(type(mpc.run(mpc.output(secnum(0), raw=True))), secnum.field)
self.assertEqual(mpc.run(mpc.output(mpc._reshare([secnum(0)]))), [0])
self.assertEqual(mpc.run(mpc.output(mpc.all(secnum(1) for _ in range(5)))), True)
self.assertEqual(mpc.run(mpc.output(mpc.all([secnum(1), secnum(1), secnum(0)]))), False)
self.assertEqual(mpc.run(mpc.output(mpc.any(secnum(0) for _ in range(5)))), False)
self.assertEqual(mpc.run(mpc.output(mpc.any([secnum(0), secnum(1), secnum(1)]))), True)
self.assertEqual(mpc.run(mpc.output(mpc.sum([secnum(1)], start=1))), 2)
self.assertEqual(mpc.run(mpc.output(mpc.prod([secnum(1)], start=1))), 1)
self.assertEqual(mpc.run(mpc.output(mpc.sum([secnum(1)], start=secnum(1)))), 2)
self.assertEqual(mpc.run(mpc.output(mpc.min(secint(i) for i in range(-1, 2, 1)))), -1)
self.assertEqual(mpc.run(mpc.output(mpc.argmin(secint(i) for i in range(-1, 2, 1))[0])), 0)
self.assertEqual(mpc.run(mpc.output(mpc.max(secfxp(i) for i in range(-1, 2, 1)))), 1)
self.assertEqual(mpc.run(mpc.output(mpc.argmax(secfxp(i) for i in range(-1, 2, 1))[0])), 2)
self.assertEqual(mpc.run(mpc.output(list(mpc.min_max(map(secfxp, range(5)))))), [0, 4])
x = (secint(i) for i in range(-3, 3))
s = [0, -1, 1, -2, 2, -3]
self.assertEqual(mpc.run(mpc.output(mpc.sorted(x, key=lambda a: a*(2*a+1)))), s)
x = (secfxp(i) for i in range(5))
self.assertEqual(mpc.run(mpc.output(mpc.sorted(x, reverse=True))), [4, 3, 2, 1, 0])
self.assertEqual(mpc.run(mpc.output(mpc.sum(map(secint, range(5))))), 10)
self.assertEqual(mpc.run(mpc.output(mpc.sum([secfxp(2.75)], start=3.125))), 5.875)
self.assertEqual(int(mpc.run(mpc.output(mpc.prod(map(secfxp, range(1, 5)))))), 24)
self.assertEqual(int(mpc.run(mpc.output(mpc.prod([secfxp(1.414214)]*4)))), 4)
def test_misc(self):
secint = mpc.SecInt()
secfxp = mpc.SecFxp()
secfld = mpc.SecFld()
for secnum in (secint, secfxp, secfld):
self.assertEqual(mpc.run(mpc.output(mpc._reshare([secnum(0)]))),
[0])
self.assertEqual(
mpc.run(mpc.output(mpc.all(secnum(1) for _ in range(5)))),
True)
self.assertEqual(
mpc.run(mpc.output(mpc.all([secnum(1),
secnum(1),
secnum(0)]))), False)
self.assertEqual(
mpc.run(mpc.output(mpc.any(secnum(0) for _ in range(5)))),
False)
self.assertEqual(
mpc.run(mpc.output(mpc.any([secnum(0),
secnum(1),
secnum(1)]))), True)
self.assertEqual(
mpc.run(mpc.output(mpc.sum([secnum(1)], start=1))), 2)
self.assertEqual(
mpc.run(mpc.output(mpc.prod([secnum(1)], start=1))), 1)
self.assertEqual(
mpc.run(mpc.output(mpc.sum([secnum(1)], start=secnum(1)))), 2)
self.assertEqual(
mpc.run(mpc.output(mpc.min(secint(i) for i in range(-1, 2, 1)))),
-1)
self.assertEqual(
mpc.run(mpc.output(mpc.max(secfxp(i) for i in range(-1, 2, 1)))),
1)
self.assertEqual(
mpc.run(mpc.output(list(mpc.min_max(map(secfxp, range(5)))))),
[0, 4])
self.assertEqual(mpc.run(mpc.output(mpc.sum(map(secint, range(5))))),
10)
self.assertEqual(
mpc.run(mpc.output(mpc.sum([secfxp(2.72)], start=3.14))), 5.86)
self.assertEqual(
int(mpc.run(mpc.output(mpc.prod(map(secfxp, range(1, 5)))))), 24)
self.assertEqual(
int(mpc.run(mpc.output(mpc.prod([secfxp(1.414214)] * 4)))), 4)
def test_empty_input(self):
secint = mpc.SecInt()
self.assertEqual(mpc.run(mpc.gather([])), [])
self.assertEqual(mpc.run(mpc.output([])), [])
self.assertEqual(mpc._reshare([]), [])
self.assertEqual(mpc.convert([], None), [])
self.assertEqual(mpc.sum([]), 0)
self.assertEqual(mpc.sum([], start=1), 1)
self.assertEqual(mpc.run(mpc.output(mpc.sum([], start=secint(1)))), 1)
self.assertEqual(mpc.prod([]), 1)
self.assertEqual(mpc.all([]), 1)
self.assertEqual(mpc.any([]), 0)
self.assertEqual(mpc.in_prod([], []), 0)
self.assertEqual(mpc.vector_add([], []), [])
self.assertEqual(mpc.vector_sub([], []), [])
self.assertEqual(mpc.scalar_mul(secint(0), []), [])
self.assertEqual(mpc.schur_prod([], []), [])
self.assertEqual(mpc.from_bits([]), 0)
async def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'-i',
'--dataset',
type=int,
metavar='I',
help=('dataset 0=uvlp (default), 1=wiki, 2=tb2x2, 3=woody, '
'4=LPExample_R20, 5=sc50b, 6=kb2, 7=LPExample'))
parser.add_argument('-l',
'--bit-length',
type=int,
metavar='L',
help='override preset bit length for dataset')
parser.set_defaults(dataset=0, bit_length=0)
args = parser.parse_args()
settings = [('uvlp', 24, 37 / 3), ('wiki', 24, 20), ('tb2x2', 18, 10.5),
('woody', 36, 540), ('LPExample_R20', 52, 3.441176),
('sc50b', 52, 70), ('kb2', 96, 1749.9204734889486),
('LPExample', 96, 1188806595)]
name, bit_length, exact_max = settings[args.dataset]
if args.bit_length:
bit_length = args.bit_length
with open(os.path.join('data', 'lp', name + '.csv')) as file:
T = list(csv.reader(file))
m = len(T) - 1
n = len(T[0]) - 1
secfxp = mpc.SecFxp(bit_length)
print(
f'Using secure {bit_length}-bit fixed-point numbers: {secfxp.__name__}'
)
print(f'dataset: {name} with {m} constraints and {n} variables')
T[0][-1] = '0' # initialize optimal value
for i in range(m + 1):
for j in range(n + 1):
T[i][j] = secfxp(float(T[i][j]), integral=False)
c = T[0][:-1] # maximize c.x subject to A.x <= b, x >= 0
A = [T[i + 1][:-1] for i in range(m)]
b = [T[i + 1][-1] for i in range(m)]
await mpc.start()
cobasis = [secfxp(j) for j in range(n)]
basis = [secfxp(n + i) for i in range(m)]
iteration = 0
while True:
# find index of pivot column
p_col_index, minimum = argmin_int(T[0][:-1])
if await mpc.output(minimum >= 0):
break # maximum reached
# find index of pivot row
p_col = mpc.matrix_prod([p_col_index], T, True)[0]
constraints = [[T[i][-1], p_col[i], p_col[i] > 0.0001]
for i in range(1, m + 1)]
p_row_index, (_, pivot, _) = argmin_rat(constraints)
# reveal progress a bit
iteration += 1
mx = await mpc.output(T[0][-1])
p = await mpc.output(pivot)
logging.info(f'Iteration {iteration}: {mx} pivot={p}')
# swap basis entries
delta = mpc.in_prod(basis, p_row_index) - mpc.in_prod(
cobasis, p_col_index)
cobasis = mpc.vector_add(cobasis, mpc.scalar_mul(delta, p_col_index))
basis = mpc.vector_sub(basis, mpc.scalar_mul(delta, p_row_index))
# update tableau Tij = Tij - (Til - bool(i==k))/Tkl * (Tkj + bool(j==l))
p_col_index.append(secfxp(0))
p_row_index.insert(0, secfxp(0))
p_col = mpc.vector_sub(p_col, p_row_index)
p_col = mpc.scalar_mul(1 / pivot, p_col)
p_row = mpc.matrix_prod([p_row_index], T)[0]
p_row = mpc.vector_add(p_row, p_col_index)
T = mpc.gauss(T, secfxp(1), p_col, p_row)
mx = await mpc.output(T[0][-1])
rel_error = (mx - exact_max) / exact_max
print(f'max = {mx} (error {rel_error:.3%}) in {iteration} iterations')
logging.info('Solution x')
x = [secfxp(0) for _ in range(n)]
for i in range(m):
u = mpc.unit_vector(basis[i], m + n)[:n]
v = mpc.scalar_mul(T[i + 1][-1], u)
x = mpc.vector_add(x, v)
cx = mpc.in_prod(c, x)
Ax = mpc.matrix_prod([x], A, True)[0]
approx = lambda a: 1.01 * a + 0.0001
Ax_bounded_by_b = mpc.all(Ax[i] <= approx(b[i]) for i in range(m))
x_nonnegative = mpc.all(x[j] >= 0 for j in range(n))
logging.info('Dual solution y')
y = [secfxp(0) for _ in range(m)]
for j in range(n):
u = mpc.unit_vector(cobasis[j], m + n)[n:]
v = mpc.scalar_mul(T[0][j], u)
y = mpc.vector_sub(y, v)
yb = mpc.in_prod(y, b)
yA = mpc.matrix_prod([y], A)[0]
approx = lambda a: mpc.if_else(a < 0, 1 / 1.01, 1.01) * a + 0.0001
yA_bounded_by_c = mpc.all(yA[j] <= approx(c[j]) for j in range(n))
y_nonpositive = mpc.all(y[i] <= 0 for i in range(m))
cx_eq_yb = abs(cx - yb) <= 0.01 * abs(cx)
check = mpc.all([
cx_eq_yb, Ax_bounded_by_b, x_nonnegative, yA_bounded_by_c,
y_nonpositive
])
check = bool(await mpc.output(check))
print(
f'verification c.x == y.b, A.x <= b, x >= 0, y.A <= c, y <= 0: {check}'
)
x = await mpc.output(x)
print(f'solution = {x}')
await mpc.shutdown()
def test_misc(self):
secint = mpc.SecInt()
secfxp = mpc.SecFxp()
secfld = mpc.SecFld()
for secnum in (secint, secfxp, secfld):
self.assertEqual(type(mpc.run(mpc.output(secnum(0), raw=True))),
secnum.field)
self.assertEqual(mpc.run(mpc.output(mpc._reshare([secnum(0)]))),
[0])
self.assertEqual(
mpc.run(mpc.output(mpc.all(secnum(1) for _ in range(5)))),
True)
self.assertEqual(
mpc.run(mpc.output(mpc.all([secnum(1),
secnum(1),
secnum(0)]))), False)
self.assertEqual(
mpc.run(mpc.output(mpc.any(secnum(0) for _ in range(5)))),
False)
self.assertEqual(
mpc.run(mpc.output(mpc.any([secnum(0),
secnum(1),
secnum(1)]))), True)
self.assertEqual(
mpc.run(mpc.output(mpc.sum([secnum(1)], start=1))), 2)
self.assertEqual(
mpc.run(mpc.output(mpc.prod([secnum(1)], start=1))), 1)
self.assertEqual(
mpc.run(mpc.output(mpc.sum([secnum(1)], start=secnum(1)))), 2)
self.assertEqual(
mpc.run(mpc.output(mpc.find([secnum(1)], 0, e=-1))), -1)
self.assertEqual(mpc.run(mpc.output(mpc.find([secnum(1)], 1))), 0)
self.assertEqual(
mpc.run(mpc.output(mpc.find([secnum(1)], 1, f=lambda i: i))),
0)
self.assertEqual(
mpc.run(mpc.output(mpc.min(secint(i) for i in range(-1, 2, 1)))),
-1)
self.assertEqual(
mpc.run(
mpc.output(mpc.argmin(secint(i) for i in range(-1, 2, 1))[0])),
0)
self.assertEqual(
mpc.run(mpc.output(mpc.max(secfxp(i) for i in range(-1, 2, 1)))),
1)
self.assertEqual(
mpc.run(
mpc.output(mpc.argmax(secfxp(i) for i in range(-1, 2, 1))[0])),
2)
self.assertEqual(
mpc.run(mpc.output(list(mpc.min_max(map(secfxp, range(5)))))),
[0, 4])
x = (secint(i) for i in range(-3, 3))
s = [0, -1, 1, -2, 2, -3]
self.assertEqual(
mpc.run(mpc.output(mpc.sorted(x, key=lambda a: a * (2 * a + 1)))),
s)
x = (secfxp(i) for i in range(5))
self.assertEqual(mpc.run(mpc.output(mpc.sorted(x, reverse=True))),
[4, 3, 2, 1, 0])
self.assertEqual(mpc.run(mpc.output(mpc.sum(map(secint, range(5))))),
10)
self.assertEqual(
mpc.run(mpc.output(mpc.sum([secfxp(2.75)], start=3.125))), 5.875)
self.assertEqual(
int(mpc.run(mpc.output(mpc.prod(map(secfxp, range(1, 5)))))), 24)
self.assertEqual(
int(mpc.run(mpc.output(mpc.prod([secfxp(1.414214)] * 4)))), 4)
self.assertEqual(mpc.find([], 0), 0)
self.assertEqual(mpc.find([], 0, e=None), (1, 0))
self.assertEqual(
mpc.run(mpc.output(list(mpc.find([secfld(1)], 1, e=None)))),
[0, 0])
self.assertEqual(
mpc.run(mpc.output(mpc.find([secfld(2)], 2, bits=False))), 0)
x = [secint(i) for i in range(5)]
f = lambda i: [i**2, 3**i]
self.assertEqual(mpc.run(mpc.output(mpc.find(x, 2, bits=False, f=f))),
[4, 9])
cs_f = lambda b, i: [b * (2 * i + 1) + i**2, (b * 2 + 1) * 3**i]
self.assertEqual(
mpc.run(mpc.output(mpc.find(x, 2, bits=False, cs_f=cs_f))), [4, 9])
async def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'-i',
'--dataset',
type=int,
metavar='I',
help=('dataset 0=uvlp (default), 1=wiki, 2=tb2x2, 3=woody, '
'4=LPExample_R20, 5=sc50b, 6=kb2, 7=LPExample'))
parser.add_argument('-l',
'--bit-length',
type=int,
metavar='L',
help='override preset bit length for dataset')
parser.set_defaults(dataset=0, bit_length=0)
args = parser.parse_args()
settings = [('uvlp', 8, 1, 2), ('wiki', 6, 1, 2), ('tb2x2', 6, 1, 2),
('woody', 8, 1, 3), ('LPExample_R20', 70, 1, 5),
('sc50b', 104, 10, 55), ('kb2', 536, 100000, 106),
('LPExample', 110, 1, 178)]
name, bit_length, scale, n_iter = settings[args.dataset]
if args.bit_length:
bit_length = args.bit_length
with open(os.path.join('data', 'lp', name + '.csv')) as file:
T = list(csv.reader(file))
m = len(T) - 1
n = len(T[0]) - 1
secint = mpc.SecInt(bit_length, n=m +
n) # force existence of Nth root of unity, N>=m+n
print(f'Using secure {bit_length}-bit integers: {secint.__name__}')
print(
f'dataset: {name} with {m} constraints and {n} variables (scale factor {scale})'
)
T[0][-1] = '0' # initialize optimal value
for i in range(m + 1):
g = 0
for j in range(n + 1):
T[i][j] = int(scale * float(T[i][j])) # scale to integer
g = math.gcd(g, T[i][j])
g = max(g, 1) if i else 1 # skip cost row
for j in range(n + 1):
T[i][j] = secint(T[i][j] // g)
c = T[0][:-1] # maximize c.x subject to A.x <= b, x >= 0
A = [T[i + 1][:-1] for i in range(m)]
b = [T[i + 1][-1] for i in range(m)]
Zp = secint.field
N = Zp.nth
w = Zp.root # w is an Nth root of unity in Zp, where N >= m + n
w_powers = [Zp(1)]
for _ in range(N - 1):
w_powers.append(w_powers[-1] * w)
assert w_powers[-1] * w == 1
await mpc.start()
cobasis = [secint(w_powers[-j]) for j in range(n)]
basis = [secint(w_powers[-(i + n)]) for i in range(m)]
previous_pivot = secint(1)
iteration = 0
while True:
# find index of pivot column
p_col_index, minimum = argmin_int(T[0][:-1])
if await mpc.output(minimum >= 0):
break # maximum reached
# find index of pivot row
p_col = mpc.matrix_prod([p_col_index], T, True)[0]
constraints = [[T[i][-1] + (p_col[i] <= 0), p_col[i]]
for i in range(1, m + 1)]
p_row_index, (_, pivot) = argmin_rat(constraints)
# reveal progress a bit
iteration += 1
mx = await mpc.output(T[0][-1])
cd = await mpc.output(previous_pivot)
p = await mpc.output(pivot) # NB: no await in f-strings in Python 3.6
logging.info(
f'Iteration {iteration}/{n_iter}: {mx / cd} pivot={p / cd}')
# swap basis entries
delta = mpc.in_prod(basis, p_row_index) - mpc.in_prod(
cobasis, p_col_index)
cobasis = mpc.vector_add(cobasis, mpc.scalar_mul(delta, p_col_index))
basis = mpc.vector_sub(basis, mpc.scalar_mul(delta, p_row_index))
# update tableau Tij = Tij*Tkl/Tkl' - (Til/Tkl' - bool(i==k)) * (Tkj + bool(j==l)*Tkl')
p_col_index.append(secint(0))
p_row_index.insert(0, secint(0))
pp_inv = 1 / previous_pivot
p_col = mpc.scalar_mul(pp_inv, p_col)
p_col = mpc.vector_sub(p_col, p_row_index)
p_row = mpc.matrix_prod([p_row_index], T)[0]
p_row = mpc.vector_add(p_row,
mpc.scalar_mul(previous_pivot, p_col_index))
T = mpc.gauss(T, pivot * pp_inv, p_col, p_row)
previous_pivot = pivot
mx = await mpc.output(T[0][-1])
cd = await mpc.output(previous_pivot
) # common denominator for all entries of T
print(
f'max = {mx} / {cd} / {scale} = {mx / cd / scale} in {iteration} iterations'
)
logging.info('Solution x')
sum_x_powers = [secint(0) for _ in range(N)]
for i in range(m):
x_powers = pow_list(T[i + 1][-1] / N, basis[i], N)
sum_x_powers = mpc.vector_add(sum_x_powers, x_powers)
x = [None] * n
for j in range(n):
coefs = [w_powers[(j * k) % N] for k in range(N)]
x[j] = mpc.in_prod(coefs, sum_x_powers)
cx = mpc.in_prod(c, x)
Ax = mpc.matrix_prod([x], A, True)[0]
Ax_bounded_by_b = mpc.all(Ax[i] <= b[i] * cd for i in range(m))
x_nonnegative = mpc.all(x[j] >= 0 for j in range(n))
logging.info('Dual solution y')
sum_x_powers = [secint(0) for _ in range(N)]
for j in range(n):
x_powers = pow_list(T[0][j] / N, cobasis[j], N)
sum_x_powers = mpc.vector_add(sum_x_powers, x_powers)
y = [None] * m
for i in range(m):
coefs = [w_powers[((n + i) * k) % N] for k in range(N)]
y[i] = mpc.in_prod(coefs, sum_x_powers)
y[i] = -y[i]
yb = mpc.in_prod(y, b)
yA = mpc.matrix_prod([y], A)[0]
yA_bounded_by_c = mpc.all(yA[j] <= c[j] * cd for j in range(n))
y_nonpositive = mpc.all(y[i] <= 0 for i in range(m))
cx_eq_yb = cx == yb
check = mpc.all([
cx_eq_yb, Ax_bounded_by_b, x_nonnegative, yA_bounded_by_c,
y_nonpositive
])
check = bool(await mpc.output(check))
print(
f'verification c.x == y.b, A.x <= b, x >= 0, y.A <= c, y <= 0: {check}'
)
x = await mpc.output(x)
print(f'solution = {[a / cd for a in x]}')
await mpc.shutdown()
m = len(mpc.parties)
if m % 2 == 0:
print('Odd number of parties required.')
sys.exit()
t = m // 2
voters = list(range(t + 1)) # parties P[0],...,P[t]
if mpc.pid in voters:
vote = int(sys.argv[1]) if sys.argv[1:] else 1 # default "yes"
else:
vote = None # no input
secbit = mpc.SecFld(2) # secure bits over GF(2)
mpc.run(mpc.start())
votes = mpc.input(secbit(vote), senders=voters)
result = mpc.run(mpc.output(mpc.all(votes), receivers=voters))
mpc.run(mpc.shutdown())
if result is None: # no output
print('Thanks for serving as oblivious matchmaker;)')
elif result:
print(
f'Match: unanimous agreement between {t+1} part{"ies" if t else "y"}!')
else:
print(
f'No match: someone disagrees among {t+1} part{"ies" if t else "y"}?')
