def distribution_to_TeX(p):
"""Formats distribution for use in TeX file.
Args:
p: A dictionary with entries for 'distr', 'sigma', 'a', 'D'
Returns:
LaTeX string.
"""
distr, sigma, a, distr_name = p['distr'], p['sigma'], p['a'], p['D']
n = max(distr.iterkeys()) + 1 # range is [0..n)
b = bits_needed_to_sample(distr)
ret = "${}$ & {} & {:.2f} & ".format(distr_name, b + 1, sigma**2)
for i in sorted(distr.iterkeys()):
if i >= 0:
p = int(round(distr[i] * 2**b))
if i == 0:
p *= 2
ret += r"\!\!{}\!\!&".format(p)
for i in xrange(max(distr.iterkeys()), 6):
ret += "&"
divergence = renyi(distr, nonnegative_half(dgauss(sigma)), a)
ret += " {:.1f} & {:.7f}".format(a, divergence)
return ret + r" \\"
def print_intermediate_result(opt_d, qlog, n, w, sigma, bandwidth,
heuristic_pr_failure, actual_pr_failure,
costs_gauss, costs_opt):
print distr_to_str(nonnegative_half(opt_d))
print "q = 2**{}, n = {}, w = {}, sigma^2 = {:.2f}:".format(
qlog, n, w, sigma**2),
print "bandwidth = {:.2f} KB,".format(bandwidth / (8. * 1000)),
print(
"heuristic Pr of failure = {:.1f}, "
"actual Pr of failure = {:.1f},".format(
log(heuristic_pr_failure, 2), log(actual_pr_failure, 2))),
formatted_costs = ", ".join("{:.1f}".format(c) for c in costs_gauss
if not isinf(c))
print "security = [{}],".format(formatted_costs),
if reduce_to_LWE or not binomials_only:
formatted_costs_opt = ", ".join("{:.1f}".format(c)
for c in costs_opt if not isinf(c))
print "security after reduction = [{}],".format(
formatted_costs_opt),
if binomials_only:
bits = int(round(4 * sigma**2)) + 1
else:
bits = bits_needed_to_sample(opt_d)
print "distribution on [{}, {}], {} bits to sample".format(
min(opt_d.iterkeys()), max(opt_d.iterkeys()), bits)
print "Parameters for print_tables.py: 'sigma': sqrt({:.2f}), 'n': {}, 'q': {}, 'B': {}, 'bits': {}, 'base': {:.0f}".format(
sigma**2, n, qlog, w, bits, min(costs_gauss))
def distribution_to_tex(p):
"""Formats distribution for use in TeX file.
Args:
p: A dictionary with entries for 'distr', 'sigma', 'a', 'name'
Returns:
LaTeX string.
"""
distr, sigma, a, distr_name = p['distr'], p['sigma'], p['a'], p['name']
dg = dgauss(sigma)
b = bits_needed_to_sample(distr)
ret = '{} & {:.3f} &'.format(distr_name, sigma)
for i in sorted(distr.iterkeys()):
if i >= 0:
p = int(round(distr[i] * 2**b))
if i == 0:
p *= 2
ret += r'\!\!{}\!\!&'.format(p)
for i in range(max(distr.iterkeys()), 12):
ret += '&'
divergence = renyi(distr, nonnegative_half(dg), a)
ret += r' {:.1f} & ${:.2f}\times 10^{{-4}}$'.format(a, divergence * 10**4)
return ret + r' \\'
def print_intermediate_result(opt_d, qlog, n, w, m_bar, n_bar, sigma,
bandwidth, heuristic_pr_failure,
actual_pr_failure, costs_gauss, costs_opt):
"""A printer helper.
"""
print(distr_to_str(nonnegative_half(opt_d)))
print(
'q = 2**{}, n = {}, w = {}, mbar x nbar = {} x {}, sigma = {:.3f}:'
.format(qlog, n, w, m_bar, n_bar, sigma),
end='')
print('bandwidth = {:.2f} kB,'.format(bandwidth / (8. * 1000))),
print(
'heuristic Pr of failure = {:.1f}, '
'actual Pr of failure = {:.1f},'.format(
log(heuristic_pr_failure) / log(2), # can be 0
log(actual_pr_failure) / log(2)), # can be 0
end='')
formatted_costs = ', '.join('{:.1f}'.format(c) for c in costs_gauss
if not isinf(c))
print('security = [{}],'.format(formatted_costs), end='')
formatted_costs_opt = ', '.join('{:.1f}'.format(c) for c in costs_opt
if not isinf(c))
print('security after reduction = [{}],'.format(formatted_costs_opt),
end='')
bits = bits_needed_to_sample(opt_d)
print('distribution on [{}, {}], {} bits to sample'.format(
min(opt_d.iterkeys()), max(opt_d.iterkeys()), bits))
print("Parameters for print_tables_pke.py: 'sigma': {:.3f}, 'n': {}, "
"'m_bar':{}, 'n_bar':{}, 'q': {}, 'B': {}, 'key_len': {}, "
"'rand_bits': {}, 'sec_base': {:.0f}".format(
sigma, n, m_bar, n_bar, qlog, w, agree_bits, bits,
min(costs_gauss)))