def PrivCheck(df_train, df_test, df_test_rec_items, age_group_number,
cluster_num, deltaX, age_list, age_group_dict, group_age_dict,
pp):
funcs.update_age_group(df_train, age_group_dict)
# random forest
model_rf = funcs.train_rf_model(df_train)
# xgboost
model_xgb = funcs.train_xgb_model(df_train)
pd.DataFrame(funcs.cal_pgy_withAgeGroup(
df_test, cluster_num, 1,
age_list)).to_csv('tmp/pgy_ageGroup_privcheck.csv',
index=False,
header=None)
funcs.update_age_group(df_test, age_group_dict)
JSD_Mat_dict = np.zeros((cluster_num, cluster_num, age_group_number))
group_min_age_dict = {}
group_usersize_dict = {}
for ag in range(age_group_number):
group_min_age_dict[ag] = group_age_dict[ag][0]
df_test_ag = df_test.loc[df_test['age_group'] == ag]
age_list_ag = group_age_dict[ag]
group_usersize_dict[ag] = df_test_ag.shape[0]
JSD_Mat_dict[:, :, ag] = funcs.cal_JSD_Matrix_withoutAgeGroup(
df_test_ag, cluster_num, 4)
scipy.io.savemat('tmp/JSDM_ageGroup_privcheck.mat',
{"JSD_Mat_input": JSD_Mat_dict})
pd.DataFrame(JSD_Mat_dict[ag]).to_csv('tmp/JSDM_ageGroup_yang.csv',
index=False,
header=None)
eng = matlab.engine.start_matlab()
eng.edit('../../matlab/age_clusternum_scenario_I/PrivCheck', nargout=0)
eng.cd('../../matlab/age_clusternum_scenario_I', nargout=0)
xpgg, distortion_budget = np.array(eng.PrivCheck(deltaX, nargout=2))
xpgg = np.array(xpgg)
df_test['age_group'] = pd.Series(np.zeros(df_test.shape[0]),
index=df_test.index,
dtype='int32')
funcs.update_age_group(df_test_rec_items, age_group_dict)
X_obf_dict = {}
for i in range(100):
X_obf_dict[i], _ = funcs.get_obf_X(df_test, xpgg, pp)
_, X_ori = funcs.get_obf_X(df_test, xpgg, pp)
for i in X_ori.keys():
user_age = X_ori[i][-2]
X_ori[i][-3] = age_group_dict[user_age]
for j in range(1):
X_obf_dict[j][i][-1] = age_group_dict[user_age]
return X_obf_dict, X_ori, model_rf, model_xgb
def PrivCheck(deltaX, cluster_num, age_group_number, df_cluster, df_train,
age_list, age_group_dict, group_age_dict, pp):
# pd.DataFrame(funcs.cal_JSD_Matrix_withAgeGroup(df_cluster, cluster_num, 1, 4)).to_csv('tmp/JSDM_ageGroup_privcheck.csv', index=False, header=None)
pd.DataFrame(funcs.cal_pgy_withAgeGroup(
df_train, cluster_num, 1,
age_list)).to_csv('tmp/pgy_ageGroup_privcheck.csv',
index=False,
header=None)
funcs.update_age_group(df_train, age_group_dict)
JSD_Mat_dict = np.zeros((cluster_num, cluster_num, age_group_number))
group_min_age_dict = {}
group_usersize_dict = {}
for ag in range(age_group_number):
group_min_age_dict[ag] = group_age_dict[ag][0]
df_train_ag = df_train.loc[df_train['age_group'] == ag]
age_list_ag = group_age_dict[ag]
group_usersize_dict[ag] = df_train_ag.shape[0]
JSD_Mat_dict[:, :, ag] = funcs.cal_JSD_Matrix_withoutAgeGroup(
df_train_ag, cluster_num, 4)
scipy.io.savemat('tmp/JSDM_ageGroup_privcheck.mat',
{"JSD_Mat_input_origin": JSD_Mat_dict})
# pd.DataFrame(JSD_Mat_dict[ag]).to_csv('tmp/JSDM_ageGroup_privcheck.csv', index=False, header=None)
### Calculate obfuscation matrix xpgg via matlab
eng = matlab.engine.start_matlab()
eng.edit('../../matlab/age_tradeoff_scenario_II/PrivCheck', nargout=0)
eng.cd('../../matlab/age_tradeoff_scenario_II', nargout=0)
xpgg, distortion_budget = np.array(eng.PrivCheck(deltaX, nargout=2))
xpgg = np.array(xpgg)
df_train['age_group'] = pd.Series(np.zeros(df_train.shape[0]),
index=df_train.index,
dtype='int32')
X_obf_dict = {}
for i in range(25):
X_obf_dict[i], _ = funcs.get_obf_X(df_train, xpgg, pp)
_, X_ori = funcs.get_obf_X(df_train, xpgg, pp)
return X_obf_dict, X_ori
df_item_age_uid, group_age_dict, k_threshold,
np.log(l_threshold), beta, alpha)
min_group = 0
for i in adjustable_groups:
age_group_dict_cur = {}
for group, group_age_list in adjustable_groups[
i].items():
for age in group_age_list:
age_group_dict_cur[age] = group
df_test_new = update_age_group(
df_test, age_group_dict_cur)
new_JSD_Mat = funcs.cal_JSD_Matrix_withAgeGroup(
df_test_new, cluster_num, age_group_number, 4)
new_pgy = funcs.cal_pgy_withAgeGroup(
df_test_new, cluster_num, age_group_number,
age_list)
new_mean_Utility = funcs.Mean_JSD(
new_JSD_Mat, xpgg)
new_mean_Privacy = funcs.Mean_KL_div(new_pgy, xpgg)
if new_mean_Utility < min_mean_Utility and new_mean_Privacy < min_mean_Privacy:
min_mean_Utility = new_mean_Utility
min_mean_Privacy = new_mean_Privacy
min_group_age_dict = copy.deepcopy(
adjustable_groups[i])
min_age_group_dict = copy.deepcopy(
age_group_dict_cur)
min_JSD_Mat = new_JSD_Mat
min_pgy = new_pgy
min_group = i
print(op, i, min_group, mean_Privacy, mean_Utility,
def HyObscure(df_train, df_test, df_test_rec_items, df_item_age_uid,
age_group_dict, group_age_dict, cluster_num, age_group_number,
age_list, deltaX, k_threshold, l_threshold, pp):
df_test_copy = copy.deepcopy(df_test)
df_test_copy['age_group'] = pd.Series(np.zeros(df_test_copy.shape[0]),
index=df_test_copy.index,
dtype='int32')
xpgg = np.ones((cluster_num * age_group_number,
cluster_num * age_group_number)) * 0.00000001
JSD_Mat = np.ones(
(cluster_num * age_group_number, cluster_num * age_group_number))
pgy = np.ones((len(age_list), cluster_num * age_group_number)) * 0.00000001
group_min_age_dict = {}
group_usersize_dict = {}
for op in range(0, 5):
age_xpgg_dict = {}
###### Compute JSD, pgy, xpgg
JSD_Mat_dict = {}
pgy_dict = {}
for ag in range(age_group_number):
group_min_age_dict[ag] = group_age_dict[ag][0]
print(group_min_age_dict[ag])
df_test_ag = df_test.loc[df_test['age_group'] == ag]
age_list_ag = group_age_dict[ag]
group_usersize_dict[ag] = df_test_ag.shape[0]
JSD_Mat_dict[ag] = funcs.cal_JSD_Matrix_withoutAgeGroup(
df_test_ag, cluster_num, 4)
print(ag, cluster_num, age_list_ag)
pgy_dict[ag] = funcs.cal_pgy_withoutAgeGroup(
df_test_ag, cluster_num, age_list_ag)
pd.DataFrame(JSD_Mat_dict[ag]).to_csv(
'tmp/JSDM_ageGroup_hyobscure.csv', index=False, header=None)
pd.DataFrame(pgy_dict[ag]).to_csv('tmp/pgy_ageGroup_hyobscure.csv',
index=False,
header=None)
eng = matlab.engine.start_matlab()
eng.edit('../../matlab/age_clusternum_scenario_I/HyObscure',
nargout=0)
eng.cd('../../matlab/age_clusternum_scenario_I', nargout=0)
age_xpgg_dict[ag], distortion_budget = np.array(
eng.HyObscure(deltaX, nargout=2))
age_xpgg_dict[ag] = np.array(age_xpgg_dict[ag])
for ag in range(age_group_number):
for age in group_age_dict[ag]:
for col in range(cluster_num):
pgy[age - group_min_age_dict[0], ag + col * age_group_number] = pgy_dict[ag][age -
group_min_age_dict[
ag], col] * \
group_usersize_dict[
ag] / \
df_test.shape[0]
for ag in range(age_group_number):
for row in range(cluster_num):
for col in range(cluster_num):
xpgg[ag + row * age_group_number,
ag + col * age_group_number] = age_xpgg_dict[ag][row,
col]
JSD_Mat[ag + row * age_group_number, ag +
col * age_group_number] = JSD_Mat_dict[ag][row,
col]
# pd.DataFrame(xpgg).to_csv('xpgg.csv', index=False, header=None)
# pd.DataFrame(pgy).to_csv('pgy_full.csv', index=False, header=None)
# pd.DataFrame(JSD_Mat).to_csv('JSD_full.csv', index=False, header=None)
min_JSD_Mat = JSD_Mat
min_pgy = pgy
### change age group by greedy approach
mean_Utility = funcs.Mean_JSD(JSD_Mat, xpgg)
mean_Privacy = funcs.Mean_KL_div(pgy, xpgg)
min_mean_Utility = mean_Utility
min_mean_Privacy = mean_Privacy
adjustable_groups, reducible_groups = funcs.age_group_adjust_greedy(
df_item_age_uid, group_age_dict, k_threshold, np.log(l_threshold))
min_group = 0
for i in adjustable_groups:
age_group_dict_cur = {}
for group, group_age_list in adjustable_groups[i].items():
for age in group_age_list:
age_group_dict_cur[age] = group
df_test_new = funcs.update_age_group(df_test, age_group_dict_cur)
new_JSD_Mat = funcs.cal_JSD_Matrix_withAgeGroup(
df_test_new, cluster_num, age_group_number, 4)
new_pgy = funcs.cal_pgy_withAgeGroup(df_test_new, cluster_num,
age_group_number, age_list)
new_mean_Utility = funcs.Mean_JSD(new_JSD_Mat, xpgg)
new_mean_Privacy = funcs.Mean_KL_div(new_pgy, xpgg)
if new_mean_Utility < min_mean_Utility and new_mean_Privacy < min_mean_Privacy:
min_mean_Utility = new_mean_Utility
min_mean_Privacy = new_mean_Privacy
min_group_age_dict = copy.deepcopy(adjustable_groups[i])
min_age_group_dict = copy.deepcopy(age_group_dict_cur)
min_JSD_Mat = new_JSD_Mat
min_pgy = new_pgy
min_group = i
print(op, i, min_group, mean_Privacy, mean_Utility,
min_mean_Privacy, min_mean_Utility, new_mean_Privacy,
new_mean_Utility)
if min_mean_Privacy < mean_Privacy and min_mean_Utility < mean_Utility:
print("find a better age group:", group_age_dict)
age_group_dict = min_age_group_dict
group_age_dict = min_group_age_dict
df_test = funcs.update_age_group(df_test, age_group_dict)
else:
break
user_num = df_test_copy.shape[0]
X_ori = {}
for k in range(user_num):
user_id = df_test_copy['uid'][k]
X_ori[user_id] = df_test_copy[df_test_copy['uid'] == user_id].values[
0, :-1]
for k in X_ori.keys():
user_age = X_ori[k][-2]
X_ori[k][-3] = age_group_dict[user_age]
df_test = funcs.update_age_group(df_test, age_group_dict)
df_train = funcs.update_age_group(df_train, age_group_dict)
df_test_rec_items = funcs.update_age_group(df_test_rec_items,
age_group_dict)
model_rf = funcs.train_rf_model(df_train)
model_xgb = funcs.train_xgb_model(df_train)
print("model train over, start obfuscating...")
X_obf_dict = {}
for i in range(100):
X_obf_dict[i], _ = funcs.get_obf_X(df_test, xpgg, pp)
return X_obf_dict, X_ori, model_rf, model_xgb
def YGen(df_train, age_group_number, cluster_num, age_list, age_group_dict,
group_age_dict, df_item_age_uid, deltaX, k_threshold, l_threshold,
pp):
df_train_copy = copy.deepcopy(df_train)
df_train_copy['age_group'] = pd.Series(np.zeros(df_train_copy.shape[0]),
index=df_train_copy.index,
dtype='int32')
user_num = df_train_copy.shape[0]
X_ori = {}
for k in range(user_num):
user_id = df_train_copy['uid'][k]
X_ori[user_id] = df_train_copy[df_train_copy['uid'] == user_id].values[
0, :-1]
for k in X_ori.keys():
user_age = X_ori[k][-2]
X_ori[k][-3] = age_group_dict[user_age]
xpgg = np.ones((cluster_num * age_group_number,
cluster_num * age_group_number)) * 0.00000001
JSD_Mat = np.ones(
(cluster_num * age_group_number, cluster_num * age_group_number))
pgy = np.ones((len(age_list), cluster_num * age_group_number)) * 0.00000001
group_min_age_dict = {}
group_usersize_dict = {}
age_xpgg_dict = {}
JSD_Mat_dict = {}
pgy_dict = {}
for ag in range(age_group_number):
group_min_age_dict[ag] = group_age_dict[ag][0]
print(group_min_age_dict[ag])
df_train_ag = df_train.loc[df_train['age_group'] == ag]
age_list_ag = group_age_dict[ag]
group_usersize_dict[ag] = df_train_ag.shape[0]
JSD_Mat_dict[ag] = funcs.cal_JSD_Matrix_withoutAgeGroup(
df_train_ag, cluster_num, 4)
pgy_dict[ag] = funcs.cal_pgy_withoutAgeGroup(df_train_ag, cluster_num,
age_list_ag)
# print(JSD_Mat_dict[ag].shape)
# print(pgy_dict[ag].shape)
pd.DataFrame(JSD_Mat_dict[ag]).to_csv('tmp/JSDM_ageGroup_ygen.csv',
index=False,
header=None)
pd.DataFrame(pgy_dict[ag]).to_csv('tmp/pgy_ageGroup_ygen.csv',
index=False,
header=None)
eng = matlab.engine.start_matlab()
eng.edit('../../matlab/age_tradeoff_scenario_II/YGen', nargout=0)
eng.cd('../../matlab/age_tradeoff_scenario_II', nargout=0)
age_xpgg_dict[ag], distortion_budget = np.array(
eng.YGen(deltaX, nargout=2))
age_xpgg_dict[ag] = np.array(age_xpgg_dict[ag])
for ag in range(age_group_number):
for age in group_age_dict[ag]:
for col in range(cluster_num):
pgy[age - group_min_age_dict[0], ag + col * age_group_number] = pgy_dict[ag][
age - group_min_age_dict[
ag], col] * \
group_usersize_dict[ag] / \
df_train.shape[0]
for ag in range(age_group_number):
for row in range(cluster_num):
for col in range(cluster_num):
xpgg[ag + row * age_group_number,
ag + col * age_group_number] = age_xpgg_dict[ag][row, col]
JSD_Mat[ag + row * age_group_number,
ag + col * age_group_number] = JSD_Mat_dict[ag][row,
col]
JSD_Mat = np.ones(
(cluster_num * age_group_number, cluster_num * age_group_number))
pgy = np.ones((len(age_list), cluster_num * age_group_number)) * 0.00000001
group_min_age_dict = {}
group_usersize_dict = {}
JSD_Mat_dict = {}
pgy_dict = {}
for ag in range(age_group_number):
group_min_age_dict[ag] = group_age_dict[ag][0]
print(group_min_age_dict[ag])
df_train_ag = df_train.loc[df_train['age_group'] == ag]
age_list_ag = group_age_dict[ag]
group_usersize_dict[ag] = df_train_ag.shape[0]
JSD_Mat_dict[ag] = funcs.cal_JSD_Matrix_withoutAgeGroup(
df_train_ag, cluster_num, 4)
pgy_dict[ag] = funcs.cal_pgy_withoutAgeGroup(df_train_ag, cluster_num,
age_list_ag)
for ag in range(age_group_number):
for age in group_age_dict[ag]:
for col in range(cluster_num):
pgy[age - group_min_age_dict[0], ag + col * age_group_number] = pgy_dict[ag][
age -
group_min_age_dict[
ag], col] * \
group_usersize_dict[ag] / \
df_train.shape[0]
for ag in range(age_group_number):
for row in range(cluster_num):
for col in range(cluster_num):
# xpgg[ag + row * age_group_number, ag + col * age_group_number] = age_xpgg_dict[ag][row, col]
JSD_Mat[ag + row * age_group_number,
ag + col * age_group_number] = JSD_Mat_dict[ag][row,
col]
min_JSD_Mat = JSD_Mat
min_pgy = pgy
### change age group by greedy approach
mean_Utility = funcs.Mean_JSD(JSD_Mat, xpgg)
mean_Privacy = funcs.Mean_KL_div(pgy, xpgg)
min_mean_Utility = mean_Utility
min_mean_Privacy = mean_Privacy
adjustable_groups, reducible_groups = funcs.age_group_adjust_greedy(
df_item_age_uid, group_age_dict, k_threshold, np.log(l_threshold))
min_group = 0
print("start adjusting...")
better_group_flag = 0
for i in adjustable_groups:
age_group_dict_cur = {}
for group, group_age_list in adjustable_groups[i].items():
for age in group_age_list:
age_group_dict_cur[age] = group
df_train_new = funcs.update_age_group(df_train, age_group_dict_cur)
new_JSD_Mat = funcs.cal_JSD_Matrix_withAgeGroup(
df_train_new, cluster_num, age_group_number, 4)
new_pgy = funcs.cal_pgy_withAgeGroup(df_train_new, cluster_num,
age_group_number, age_list)
new_mean_Utility = funcs.Mean_JSD(new_JSD_Mat, xpgg)
new_mean_Privacy = funcs.Mean_KL_div(new_pgy, xpgg)
print(new_mean_Privacy)
print(new_mean_Utility)
if new_mean_Utility < min_mean_Utility and new_mean_Privacy < min_mean_Privacy:
min_mean_Utility = new_mean_Utility
min_mean_Privacy = new_mean_Privacy
min_group_age_dict = copy.deepcopy(adjustable_groups[i])
min_age_group_dict = copy.deepcopy(age_group_dict_cur)
min_JSD_Mat = new_JSD_Mat
min_pgy = new_pgy
min_group = i
print('Find better group!')
better_group_flag = 1
print(i, min_group, mean_Privacy, mean_Utility, min_mean_Privacy,
min_mean_Utility, new_mean_Privacy, new_mean_Utility)
if better_group_flag == 1:
age_group_dict = min_age_group_dict
group_age_dict = min_group_age_dict
else:
print("find better group failed.")
df_train = funcs.update_age_group(df_train, age_group_dict)
# 使用得到的xpgg求解混淆后的df_train
X_obf_dict = {}
for i in range(25):
X_obf_dict[i], _ = funcs.get_obf_X(df_train, xpgg, pp)
return X_obf_dict, X_ori