# Harmony Algorithm 2 for handling numeric attributes
t = [
float(data['secret']['value0']),
float(data['secret']['value1']),
float(data['secret']['value2'])
]
d = 3
epsilon = 2.0
# **IMPORTANT** chosen by curator in both preprocessing and postprocessing
Aj = 1
# **IMPORTANT**: Instead of using the same seed in
# preprocessing and postprocesing algorithm to have identical Aj
# the curator can simply choose the Aj
# IMPORTANT: This is unique for each agent and not accessable to the curator.
# agentAnalysisSeed = 123456789
# To reproduce results set see value accordingly
# random.seed(agentAnalysisSeed)
# Aj = random.randint(0,d)
assert Aj < d
# To reproduce results set see value accordingly
prng = np.random.RandomState(None)
# To reproduce results set see value accordingly
random.seed(None)
u = np.random.choice([1, -1], 1, p=[(t[Aj] + 1.0) / 2.0, (1.0 - t[Aj]) / 2.0])
libdp.toXml(outFile, u)
return item
try:
InLines = libdp.fromXml(preProcResult, numOutput)
except:
libdp.log("Private Policy and Clamping",
"Bad outputs, will be replaced by dummy values")
# Plece holder for the private policy
for i in range(0, numOutput):
if os.path.isfile(dataDir + str(i) + '.analysesMatrix'):
mat, fn = libdp.loadTransitionMatrix(dataDir + str(i) +
'.analysesMatrix')
# print (mat , fn , InLines)
InLines[i] = clamp(InLines[i], fn) # Maybe randomly pick one
OutLines[i] = libdp.randmizer(InLines[i], mat)
elif os.path.isfile(dataDir + str(i) + '.analysesLaplace'):
fd = open(dataDir + str(i) + '.analysesLaplace', "r")
epsilon = float(fd.readline())
try:
# Clamp into the range [+1,-1]
InLines[i] = max(min(float(InLines[i]), 1.0), -1.0)
except: # if index was not defined
InLines[i] = 0.0
OutLines[i] = str(libdp.laplace(epsilon, InLines[i]))
else:
print("wtf")
libdp.toXml(clmapedRandomisedResult, OutLines)
import random
import math
sys.path.append('/libdp')
import libdp
inFile = sys.argv[1]
outFile = sys.argv[2]
data = libdp.fromXml(inFile)
d = 3
epsilon = 2.0
# **IMPORTANT** chosen by curator in both preprocessing and postprocessing
Aj = 1
# **IMPORTANT**: Instead of using the same seed in
# preprocessing and postprocesing algorithm to have identical Aj
# the curator can simply choose the Aj
# IMPORTANT: This is unique for each agent and not accessable to the curator.
# agentAnalysisSeed = 123456789
# To reproduce results set see value accordingly
# random.seed(agentAnalysisSeed)
# Aj = random.randint(0,d)
assert Aj < d
out = [0.0] * d
out[Aj] = float(
data[0]) * float(d) * (math.exp(epsilon) + 1.0) / (math.exp(epsilon) - 1.0)
libdp.toXml(outFile, out)
import sys
import configparser
sys.path.append('/libdp')
import libdp
inFile = sys.argv[1]
outFile = sys.argv[2]
print(inFile)
data = configparser.ConfigParser()
data.read(inFile + 'data.ini')
#with open(inFile + 'data.ini', 'r') as fin:
# print (fin.read())
l = [data['secret']['cat0'], data['secret']['cat1']]
libdp.toXml(outFile, l)
# log('hash_input %r', value)
# log('Cohort %d', cohort)
# log('MD5 %s', md5.hexdigest())
# python2 :
# return [ord(digest[i]) % num_bloombits for i in xrange(num_hashes)]
# python3 :
return [digest[i] % num_bloombits for i in range(num_hashes)]
inFile = sys.argv[1]
outFile = sys.argv[2]
num_bloombits = int(sys.argv[3])
num_hashes = int(sys.argv[4])
data = configparser.ConfigParser()
data.read(inFile)
# python2 : secret = data['secret']['strx']
secret = str.encode(data['secret']['strx'])
cohort = 1
setBits = get_bloom_bits(secret, cohort, num_hashes, num_bloombits)
bloom = [0] * num_bloombits
for i in setBits:
bloom[i] = 1
libdp.log('RAPPOR', secret=secret, bloombits=setBits, bloom=bloom)
libdp.toXml(outFile, bloom)
# privacy parameter epsilon = float(sys.argv[7]) # Shared seeds between preprocessing and postprocessing to generate s SSharedRandomSeeds = int(sys.argv[8]) # This seed is used to generate identical Johnson-Lindenstrauss # random projection matrix sharepd between the agents and the curator PhiSharedRandomSeeds = int(sys.argv[9]) vi = int(data['secret'][x]) assert vi < d + 1 and vi > 0 assert beta > 0 # To avoid negative log assert beta < 2 assert epsilon > 0 assert d > 1 gamma = math.sqrt(math.log(2.0 * d / beta) / epsilon**2.0 * n) mx = math.log(float(d) + 1) * math.log(2.0 / beta) / (gamma**2) # ceil, floor or round ? # TODO: Better way to convert m to an integer! m = int(math.ceil(mx)) np.random.seed(PhiSharedRandomSeeds) phi = np.random.choice([-1.0, 1.0], size=(d, m)) # To reproduce results set see value accordingly random.seed(SSharedRandomSeeds) j = random.randint(0, m) l = phi[vi][j] libdp.toXml(outFile, [l])
import configparser
import random
import numpy as np
sys.path.append('/libdp')
import libdp
inFile = sys.argv[1]
outFile = sys.argv[2]
data = configparser.ConfigParser()
data.read(inFile)
# Duchi Algorithm 1 for handling numeric attributes
t = [
float(data['secret']['value0']),
float(data['secret']['value1']),
float(data['secret']['value2'])
]
d = 3
# To reproduce results set see value accordingly
prng = np.random.RandomState(None)
# To reproduce results set see value accordingly
random.seed(None)
for Aj in range(0, len(t)):
v = np.random.choice([1, -1],
d,
p=[(t[Aj] + 1.0) / 2.0, (1.0 - t[Aj]) / 2.0])
libdp.toXml(outFile, v)
# privacy parameter
epsilon = float(sys.argv[7])
# Shared seeds between preprocessing and postprocessing to generate s
SSharedRandomSeeds = int(sys.argv[8])
# This seed is used to generate identical
# Johnson-Lindenstrauss random projection matrix
# sharepd between the agents and the curator
PhiSharedRandomSeeds = int(sys.argv[9])
assert beta > 0
# To avoid negative log
assert beta < 2
assert epsilon > 0
assert d > 1
gamma = math.sqrt(math.log(2.0 * d / beta) / epsilon**2.0 * n)
mx = math.log(float(d) + 1) * math.log(2.0 / beta) / (gamma**2)
# ceil, floor or round ?
m = int(math.ceil(mx))
# TODO: Better way to convert m to an integer!
# To reproduce results set see value accordingly
random.seed(SSharedRandomSeeds)
j = random.randint(0, m)
zj = float(data[0]) * math.sqrt(m) * ((math.exp(epsilon) + 1) /
(math.exp(epsilon) - 1))
o = '(' + str(j) + ',' + str(zj) + ')'
libdp.toXml(outFile, [o])
