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msrigs_simulation_sensitivityanalysis_benefitchanging.py
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msrigs_simulation_sensitivityanalysis_benefitchanging.py
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# Multi-Stage Re-Identification (MSRI) Game Solver (GS) v1.1
# Component: MSRIGS Sensitivity Analysis On Benefit of Sharing All Dat Using Simulated Datasets
# (Data subjects have different strategies)
# Copyright 2018-2021 Zhiyu Wan, HIPLAB, Vanderilt University
# Compatible with python 3.8.5. Package dependencies: Numpy 1.19.1, Scikit-learn 0.23.2, Pandas 1.1.3, Matplotlib 3.3.1,
# Seaborn 0.11.0, and SciPy 1.5.2
# Update history:
# Added the surname inference algorithm from the paper "Identifying personal genomes by surname inference", Science 2013
# Nov 19, 2018: Added 3 global dictionaries for distance and confidence score computation;
# Nov 19, 2018: Deleted mulist, xlist; Let etmrca function be more efficient by using cache and pre-computing
# Nov 21, 2018: Added a global dictionary for attack simulation computation
# Nov 23, 2018: Utility for each attribute is proportional to entropy
# Nov 27, 2018: Adding generalization strategies for age and state attributes
# Dec 2, 2018: Adding weight vector for utility, and adding missing data to G
# Dec 17, 2018: Individual-wise defense strategy
# Dec 17, 2018: Adding a surname inference dictionary
# Jan 23, 2019: Deleting the dictionary for defense computation
# March 18, 2019: Fixing a bug regarding tuple_x (all changed to tuple_x_selection)
# March 12, 2020: 3)save detailed results!
# 4)add the scenario of data masking! 5)add the scenario that only opt-in or opt-out!
# 6)add the attacker's cost (attack rate * c)! 7)add the defender's cost (L*pa)!
# March 17, 2020: 8)consider the scenario of always_attack
# March 18, 2020: 2)fix the total utility for various m
# March 19, 2020: 1)threshold for theta
# April 16, 2020: To plot privacy and utility. To plot error bar.
# April 19, 2020: clean the code
# April 21, 2020: functions been moved out, changed optimal defense output
# April 21, 2020: 1) output all data points instead of just average values. 2)deleted "defense_mode" and "n_cost"
# July 15, 2020: Modify one scenario, and add three new scenarios.
# July 15, 2020: Rename saved file.
# Oct 15, 2020: Using function version 21.
# Oct 15, 2020: In the inner loop, add missing values and compute entropy (with missing data).
# May 9, 2020: save the success rate
import numpy as np
import time
import pandas as pd
import msrigs_functions as sf
import os.path
import pickle
# configuration
id_exp = '2056' # ID for the set of experiments
n_f: int = 20 # number of firstnames
n_I: int = 20000 # size of the identified dataset (<=90000)
n_S: int = 1000 # size of the sensitive dataset (<=90000)
n_G: int = 20000 # size of the genetic genealogy dataset # from 4000 on 20180928 # from 10000 on 20200312 (<=90000)
rate_s = 0.6 # rate of sensitive
loss = 150
cost = 10
#total_utility = 100
n_iter: int = 20
theta_p = 0.5
method: int = 2
m_g: int = 12 # default: 16
m_start: int = 0
n_m: int = 17
step: int = 25
weight = np.concatenate((np.ones(2) * 1, np.ones(m_g)), axis=None)
missing_level = 0.3
over_confident: int = 0
alter_weight: int = 0 # 0: Based on information entropy. 1: Uniform. 2: Special (the weight of 1st 2 geno features is 10x).
algorithm: int = 0 # 0: greedy algorithm. 1: brute-force algorithm.
pruning: int = 1
participation_rate = 0.05
random_masking_rate = 0.15
save_dic: int = 0
# choose a parameter
parameter: int = 6 # 0:Number of genomic attributes. 1:Missing proportion of genomic data. 2:Threshold for confidence score.
# 3:Number of records in the genetic genealogy dataset. 4:Number of records in the identified dataset.
# 5:Loss from being re-identified. 6:Benefit of sharing all data. 7:Cost of an attack.
parameter_names = ['m', 'missinglevel', 'theta', 'nG', 'nI', 'loss', 'benefit', 'cost']
# choose a scenario
scenario = 0 # 0: no protection. 1: no genomic data sharing. 2: random opt-in. 3: random masking. 3.x: custom masking
# 3.1: k-anonymity. 4: opt-in game. 5: masking game. 6: no-attack masking game. 7: one-stage masking game.
# creat folders
folder_result = 'Results' + id_exp + '/' + parameter_names[parameter] + 'changing/'
if pruning == 1:
folder_result += 'pruning/'
# check the existence of the directory, create if not
folders = folder_result.rstrip('/').split('/')
folder = ''
for folder_name in folders:
folder += folder_name + '/'
if not os.path.exists(folder):
os.mkdir(folder)
pickle_filename = folder_result + 'result_p' + str(parameter) + '_s' + str(scenario) + '.pickle'
filename = folder_result + 'log_p' + str(parameter) + '_s' + str(scenario) + '.txt'
mu = 1e-3 * np.array([2.381, 2.081, 1.781, 2.803, 2.298, 3.081, 0.552, 0.893, 1.498, 0.425, 5.762, 1.590,
4.769, 6.359, 3.754, 2.180]) # updated mutation rate in 2008
tol = 0.2
Ne = 10000
inv_Ne = 1.0 / Ne
T_Max = 200
I_selection = np.array([2, 3, -2]).astype(int)
if __name__ == '__main__':
start1 = time.time()
surname = []
genome = []
ages = []
states = []
ID = []
n_r = []
array_optimal_payoff = np.empty(n_m * n_iter * n_S)
array_optimal_attacker_payoff = np.empty(n_m * n_iter * n_S)
array_privacy = np.empty(n_m * n_iter * n_S)
array_utility = np.empty(n_m * n_iter * n_S)
array_success_rate = np.empty(n_m * n_iter * n_S)
dic_dist = {}
dic_score_solo = {}
dic_score = {}
for i in range(3):
n = 0
f = open("data/csv/ped"+str(i+1)+".txt", "r")
f2 = open("data/csv/surname" + str(i + 1) + ".txt", "r")
f3 = open("data/csv/birth_year" + str(i + 1) + ".txt", "r")
f4 = open("data/csv/state" + str(i + 1) + ".txt", "r")
for line in f.readlines():
line2 = f2.readline()
line3 = f3.readline()
line4 = f4.readline()
sname = int(float(line2.rstrip("\n"))) #
age = 2020-int(float(line3.rstrip("\n")))
state = int(float(line4.rstrip("\n")))
loci = line.rstrip("\n").split(" ")
if loci[3] == 'M':
n += 1
y1 = []
for j in range(len(loci)):
if j == 0:
ID.append(int(loci[j]))
elif j >= 5 and j % 2 == 0:
y1.append(int(loci[j]))
genome.append(y1)
surname.append(sname)
ages.append(age)
states.append(state)
n_r.append(n)
f.close()
f2.close()
f3.close()
f4.close()
elapsed1 = (time.time() - start1)
f = open(filename, 'w')
f.write(folder_result + '\n')
start2 = time.time()
for i in range(n_iter):
print('iter: ', i)
np.random.seed(i) # reset random number generator for comparison
World = sf.build_world(ID, genome, surname, ages, states, n_r, n_f, rate_s)
(S, I, G2) = sf.generate_datasets(World, n_I, n_S, n_G) #G2 has ground truth
# ID, first name, ages, states, genomic attributes, surname, sensitive
# Add missing values
if missing_level > 0:
np.random.seed(i)
n_missing = int(n_G * m_g * missing_level)
missed = np.append(np.zeros(n_missing).astype(int), np.ones(n_G * m_g - n_missing).astype(int))
np.random.shuffle(missed)
G1 = np.multiply(G2[:, 4:(4 + m_g)], missed.reshape(n_G, m_g))
G = np.concatenate((G2[:, 0:4], G1, G2[:, -2:]), axis=1)
else:
G = G2
# Compute entropy
if alter_weight == 0:
entropy = []
for j in range(m_g + 2):
if j == 0 or j == 1: # entropy in demographic dataset
c = I[:, j + 2]
else: # entropy in genetic genealogy dataset
c = G[:, j + 2]
entropy.append(sf.get_entropy(c))
entropy = np.asarray(entropy)
weighted_entropy = np.multiply(entropy, weight[0:(m_g + 2)])
elif alter_weight == 1:
weighted_entropy = weight[0:(m_g + 2)]
elif alter_weight == 2:
weighted_entropy = np.concatenate((np.ones(2), np.ones(2) * 10, np.ones(m_g - 2)), axis=None)
# load customized strategy
custom_strategy_folder = "custom_strategy/"
if scenario == 3.1:
custom_strategy_folder += "k_anonymity/"
custom_strategy_filename = folder_result + custom_strategy_folder + 'i' + str(i) + '.csv'
if os.path.exists(custom_strategy_filename):
custom_strategies = np.genfromtxt(custom_strategy_filename, delimiter=',').astype(bool)
else:
print(custom_strategy_filename + " does not exist!")
custom_strategies = np.ones([n_S, 2 + m_g]).astype(bool) # default
dic_surname = {}
dic_attack = {}
for m_n in range(n_m):
total_utility = step * m_n + m_start
print('total_utility: ', total_utility)
for j in range(n_S):
#print('j: ', j)
s = S[j, :]
(opt_payoff, opt_attacker_payoff, opt_attack, opt_success_rate, opt_utility, opt_strategy) = \
sf.optimal_defense(s, I, G, weighted_entropy, m_g, dic_attack, dic_surname, loss, cost, scenario,
total_utility, theta_p, over_confident, mu, method, tol, dic_dist,
dic_score_solo, dic_score, T_Max, inv_Ne, participation_rate, random_masking_rate,
algorithm, pruning, I_selection, custom_strategies[j, :], 1)
index = m_n * n_S * n_iter + i * n_S + j
array_optimal_payoff[index] = opt_payoff
array_optimal_attacker_payoff[index] = opt_attacker_payoff
array_privacy[index] = 1 - opt_success_rate * opt_attack
array_utility[index] = opt_utility
array_success_rate[index] = opt_success_rate
f.write(
'{}-{}-{}: {} {:f} {:d} {}\n'.format(i, m_n, j, np.array(list(map(int, opt_strategy))), opt_payoff,
opt_attack,
opt_success_rate))
dataset = pd.DataFrame({'privacy': array_privacy,
'utility': array_utility,
'defender_optimal': array_optimal_payoff,
'attacker_optimal': array_optimal_attacker_payoff,
'success_rate': array_success_rate})
dataset.to_pickle(pickle_filename)
elapsed2 = (time.time() - start2)
# print out the results
column_names = ['defender_optimal', 'privacy', 'utility', 'success_rate']
for i in range(4):
data = np.array(dataset[column_names[i]])
shaped_dataset = np.reshape(data, (n_m, n_iter * n_S))
av2_dataset = np.mean(shaped_dataset, axis=1)
f.write(column_names[i] + ' ' + str(av2_dataset) + '.\n')
f.write("Time used: " + str(elapsed1) + " seconds (loading) + " + str(elapsed2) + " seconds (computing).\n")
f.close()
if save_dic == 1:
# save dictionaries
dic_names = ['dist', 'score', 'score_solo', 'attack', 'surname']
dics = [dic_dist, dic_score, dic_score_solo, dic_attack, dic_surname]
for i in range(5):
f1 = open(folder_result + 'dic_p' + str(parameter) + '_s' + str(scenario) + '_' + dic_names[i] + '.pkl', 'wb')
pickle.dump(dics[i], f1, protocol=pickle.HIGHEST_PROTOCOL)
f1.close()