def centralize_and_anonymize(np_preds, num_labels, epsilon, do_pate=False):
print("Combined predictions: {}, labels: {}".format(
np_preds.shape, np.unique(np_preds)))
# all ctions done
y_train = []
n_obs = np_preds.shape[1]
print(
"Performing DP query and adding laplace noise eps={} on {} observation..."
.format(epsilon, n_obs))
for _obs in range(n_obs):
np_obs = np_preds[:, _obs]
label_counts = np.bincount(np_obs, minlength=num_labels)
label_counts = add_noise_to_preds(label_counts, eps=epsilon)
y_train.append(np.argmax(label_counts))
y_train = np.array(y_train)
print(" Labels DP done, new labels: {}".format(np.unique(y_train)))
if do_pate:
print("Performing PATE analysis on...")
data_dep_eps, data_ind_eps = pate.perform_analysis(
teacher_preds=np_preds,
indices=y_train,
noise_eps=epsilon,
delta=1e-5)
print(" Data Independent Epsilon: {:.10f}".format(data_ind_eps))
print(" Data Dependent Epsilon: {:.10f}".format(data_dep_eps))
return y_train
def test_torch_ref_match():
# Verify if the torch implementation values match the original Numpy implementation.
num_teachers, num_examples, num_labels = (100, 50, 10)
preds = (np.random.rand(num_teachers, num_examples) * num_labels).astype(
int) # fake preds
indices = (np.random.rand(num_examples) * num_labels).astype(
int) # true answers
preds[:, 0:10] *= 0
data_dep_eps, data_ind_eps = pate.perform_analysis_torch(preds,
indices,
noise_eps=0.1,
delta=1e-5)
data_dep_eps_ref, data_ind_eps_ref = pate.perform_analysis(preds,
indices,
noise_eps=0.1,
delta=1e-5)
assert torch.isclose(data_dep_eps, torch.tensor(data_dep_eps_ref.item()))
assert torch.isclose(data_ind_eps, torch.tensor(data_ind_eps_ref.item()))
def test_base_dataset():
num_teachers, num_examples, num_labels = (100, 50, 10)
preds = (np.random.rand(num_teachers, num_examples) * num_labels).astype(
int) # fake preds
indices = (np.random.rand(num_examples) * num_labels).astype(
int) # true answers
preds[:, 0:10] *= 0
data_dep_eps, data_ind_eps = pate.perform_analysis(teacher_preds=preds,
indices=indices,
noise_eps=0.1,
delta=1e-5)
assert data_dep_eps < data_ind_eps
labels = np.array([9, 9, 3, 6, 9, 9, 9, 9, 8, 2])
counts = np.bincount(labels, minlength=10)
query_result = np.argmax(counts)
print(query_result)
num_teachers, num_examples, num_labels = (100, 100, 10)
preds = (np.random.rand(num_teachers, num_examples) *
num_labels).astype(int) #fake preds
indices = (np.random.rand(num_examples) * num_labels).astype(
int) # true answers
preds[:, 0:10] *= 0
data_dep_eps, data_ind_eps = pate.perform_analysis(teacher_preds=preds,
indices=indices,
noise_eps=0.1,
delta=1e-5)
assert data_dep_eps < data_ind_eps
data_dep_eps, data_ind_eps = pate.perform_analysis(teacher_preds=preds,
indices=indices,
noise_eps=0.1,
delta=1e-5)
print("Data Independent Epsilon:", data_ind_eps)
print("Data Dependent Epsilon:", data_dep_eps)
preds[:, 0:50] *= 0
data_dep_eps, data_ind_eps = pate.perform_analysis(teacher_preds=preds,
indices=indices,
def test_section_1_differential_privacy():
"""This tests the Udacity course content found at
https://github.com/Udacity/private-ai
"""
# the number of entries in our database
num_entries = 5000
db = torch.rand(num_entries) > 0.5
db = torch.rand(num_entries) > 0.5
def get_parallel_db(db, remove_index):
return torch.cat((db[0:remove_index], db[remove_index + 1:]))
get_parallel_db(db, 52352)
def get_parallel_dbs(db):
parallel_dbs = list()
for i in range(len(db)):
pdb = get_parallel_db(db, i)
parallel_dbs.append(pdb)
return parallel_dbs
pdbs = get_parallel_dbs(db)
def create_db_and_parallels(num_entries):
db = torch.rand(num_entries) > 0.5
pdbs = get_parallel_dbs(db)
return db, pdbs
db, pdbs = create_db_and_parallels(20)
db, pdbs = create_db_and_parallels(5000)
def query(db):
return db.sum()
full_db_result = query(db)
sensitivity = 0
for pdb in pdbs:
pdb_result = query(pdb)
db_distance = torch.abs(pdb_result - full_db_result)
if db_distance > sensitivity:
sensitivity = db_distance
def sensitivity(query, n_entries=1000):
db, pdbs = create_db_and_parallels(n_entries)
full_db_result = query(db)
max_distance = 0
for pdb in pdbs:
pdb_result = query(pdb)
db_distance = torch.abs(pdb_result - full_db_result)
if db_distance > max_distance:
max_distance = db_distance
return max_distance
def query(db):
return db.float().mean()
sensitivity(query)
db, pdbs = create_db_and_parallels(20)
db
def query(db, threshold=5):
return (db.sum() > threshold).float()
for i in range(10):
sens_f = sensitivity(query, n_entries=10)
print(sens_f)
db, _ = create_db_and_parallels(100)
pdb = get_parallel_db(db, remove_index=10)
db[10]
sum(db)
# differencing attack using sum query
sum(db) - sum(pdb)
# differencing attack using mean query
(sum(db).float() / len(db)) - (sum(pdb).float() / len(pdb))
# differencing attack using threshold
(sum(db).float() > 49).float() - (sum(pdb).float() > 49).float()
def query(db):
true_result = torch.mean(db.float())
first_coin_flip = (torch.rand(len(db)) > 0.5).float()
second_coin_flip = (torch.rand(len(db)) > 0.5).float()
augmented_database = db.float() * first_coin_flip + (
1 - first_coin_flip) * second_coin_flip
db_result = torch.mean(augmented_database.float()) * 2 - 0.5
return db_result, true_result
db, pdbs = create_db_and_parallels(10)
private_result, true_result = query(db)
print("With Noise:" + str(private_result))
print("Without Noise:" + str(true_result))
db, pdbs = create_db_and_parallels(100)
private_result, true_result = query(db)
print("With Noise:" + str(private_result))
print("Without Noise:" + str(true_result))
db, pdbs = create_db_and_parallels(1000)
private_result, true_result = query(db)
print("With Noise:" + str(private_result))
print("Without Noise:" + str(true_result))
db, pdbs = create_db_and_parallels(10000)
private_result, true_result = query(db)
print("With Noise:" + str(private_result))
print("Without Noise:" + str(true_result))
def query(db, noise=0.2):
true_result = torch.mean(db.float())
first_coin_flip = (torch.rand(len(db)) > noise).float()
second_coin_flip = (torch.rand(len(db)) > 0.5).float()
augmented_database = db.float() * first_coin_flip + (
1 - first_coin_flip) * second_coin_flip
sk_result = augmented_database.float().mean()
private_result = ((sk_result / noise) - 0.5) * noise / (1 - noise)
return private_result, true_result
db, pdbs = create_db_and_parallels(100)
private_result, true_result = query(db, noise=0.1)
print("With Noise:" + str(private_result))
print("Without Noise:" + str(true_result))
db, pdbs = create_db_and_parallels(100)
private_result, true_result = query(db, noise=0.2)
print("With Noise:" + str(private_result))
print("Without Noise:" + str(true_result))
db, pdbs = create_db_and_parallels(100)
private_result, true_result = query(db, noise=0.4)
print("With Noise:" + str(private_result))
print("Without Noise:" + str(true_result))
db, pdbs = create_db_and_parallels(100)
private_result, true_result = query(db, noise=0.8)
print("With Noise:" + str(private_result))
print("Without Noise:" + str(true_result))
db, pdbs = create_db_and_parallels(10000)
private_result, true_result = query(db, noise=0.8)
print("With Noise:" + str(private_result))
print("Without Noise:" + str(true_result))
db, pdbs = create_db_and_parallels(100)
def query(db):
return torch.sum(db.float())
# def M(db):
# query(db) # + noise
#
query(db)
epsilon = 0.0001
import numpy as np
db, pdbs = create_db_and_parallels(100)
def sum_query(db):
return db.sum()
def laplacian_mechanism(db, query, sensitivity):
beta = sensitivity / epsilon
noise = torch.tensor(np.random.laplace(0, beta, 1))
return query(db) + noise
def mean_query(db):
return torch.mean(db.float())
laplacian_mechanism(db, sum_query, 1)
laplacian_mechanism(db, mean_query, 1 / 100)
import numpy as np
num_teachers = 10 # we're working with 10 partner hospitals
num_examples = 10000 # the size of OUR dataset
num_labels = 10 # number of lablels for our classifier
preds = ((np.random.rand(num_teachers, num_examples) *
num_labels).astype(int).transpose(1, 0)) # fake predictions
new_labels = list()
for an_image in preds:
label_counts = np.bincount(an_image, minlength=num_labels)
epsilon = 0.1
beta = 1 / epsilon
for i in range(len(label_counts)):
label_counts[i] += np.random.laplace(0, beta, 1)
new_label = np.argmax(label_counts)
new_labels.append(new_label)
labels = np.array([9, 9, 3, 6, 9, 9, 9, 9, 8, 2])
counts = np.bincount(labels, minlength=10)
query_result = np.argmax(counts)
query_result
from syft.frameworks.torch.differential_privacy import pate
num_teachers, num_examples, num_labels = (100, 100, 10)
preds = (np.random.rand(num_teachers, num_examples) * num_labels).astype(
int) # fake preds
indices = (np.random.rand(num_examples) * num_labels).astype(
int) # true answers
preds[:, 0:10] *= 0
data_dep_eps, data_ind_eps = pate.perform_analysis(teacher_preds=preds,
indices=indices,
noise_eps=0.1,
delta=1e-5)
assert data_dep_eps < data_ind_eps
data_dep_eps, data_ind_eps = pate.perform_analysis(teacher_preds=preds,
indices=indices,
noise_eps=0.1,
delta=1e-5)
print("Data Independent Epsilon:", data_ind_eps)
print("Data Dependent Epsilon:", data_dep_eps)
preds[:, 0:50] *= 0
data_dep_eps, data_ind_eps = pate.perform_analysis(teacher_preds=preds,
indices=indices,
noise_eps=0.1,
delta=1e-5,
moments=20)
print("Data Independent Epsilon:", data_ind_eps)
print("Data Dependent Epsilon:", data_dep_eps)
