# Distributed machine learning and partially homomorphic encryption (Part 1)

In this post, we will give a demonstration of the usage and flexibility of our python-paillier library as a tool for more secure machine learning. We will assume some basic knowledge about Paillier partially homomorphic encryption, and linear regression.

In particular, we will set up a simple secure protocol for federated machine learning, inspired by recent Google's work on the topic.

## Introduction to the API

Let's start with a quick demo of the API. First thing, let's create public and private keys by using a key length in bits long enough to get decent cryptographic guarantees:

>>> import phe as paillier
>>> pubkey, privkey = paillier.generate_paillier_keypair(n_length=1024)

Paillier is an asymmetric cryptoscheme (like RSA), where the public key is used for encryption and the private key for decryption.

>>> secret_numbers = [3.141592653, 300, -4.6e-12]
>>> encrypted_numbers = [pubkey.encrypt(x) for x in secret_numbers]
>>> [privkey.decrypt(x) for x in encrypted_numbers]
[3.141592653, 300, -4.6e-12]

But what do these encrypted numbers look like? You can open up the object and look inside at the integer representation.

>>> print(encrypted_numbers[0])
<phe.paillier.EncryptedNumber object at 0x7f02f2849dd8>
>>> print(encrypted_numbers[0].ciphertext())
5072752399058920189730182586811912902463474480667712432717959774819587074489325225214240998778150373197112637448816662931016970373407389034275190558182343858721113940870709409924017166597407543355101815707936636905640749963575027963216011646497564724153729103147138747511854121934327406877629294760278241554316409859573065893681767802219202771728963191523152254974808451269262932426358339707361034738737940843867971577772899191177890333880357061518134745146513228505813785268901991647262058355794072849790632418679961213162239495600291127208408082882305219363330327154890172539087918477378211986323886814727480557038

Paillier encryption is a great tool for preserving simple arithmetic operations in the encrypted space. We can sum two encrypted numbers and decrypt the result and that will be equal to the sum of the original numbers.

>>> x, y = 2, 0.5
>>> encrypted_x = pubkey.encrypt(x)
>>> encrypted_y = pubkey.encrypt(y)
>>> encrypted_sum = encrypted_x + encrypted_y
>>> privkey.decrypt(encrypted_sum)
2.5

In the same way, multiplication of an encrypted number by a number in the clear works.

>>> z = 10
>>> privkey.decrypt(z * encrypted_x)
20

Notice that we cannot multiply two encrypted numbers together. This is the limit of Paillier cryptosystem which is a partially homomorphic encryption scheme in contrast to fully homomorphic. Despite this limit, with those two allowed operations we can already play in an interesting space, with a subset of linear algebra useful for implementing machine learning primitives.

## Secure Federated Learning

In this example we assume we have sensitive data of 442 hospital patients, with different level of progress of diabetes. Recorded variables are age, gender, body mass index, average blood pressure, and six blood serum measurements. A last variable is a quantitative measure of the disease progression which we would like to predict from the previous variables. Since this measure is continuous, we will solve the problem by performing linear regression. The original data is hosted here and we access it via sklearn.

The data is distributed among 3 hospitals, referred as 'clients'. The objective is to make use of the whole (virtual) training set to improve upon the model that can be trained locally. Such a scenario is often referred to as 'horizontally partitioned'. Fifty patient records will be kept as a testset and not used for training. An additional agent is the 'server', who will facilitate the information exchange among the hospitals under the following constraints. Due to privacy policy:

1. The individual patients' records data at each hospital cannot leave its premises, not even in encrypted form
2. Even information/summary derived (read: gradients) from any individual client's dataset cannot leave a hospital, unless it is first encrypted.
3. None of the parties (clients AND server) must be able to infer WHERE (in which hospital) a patient in the training set has been treated.

Let's go to the code. We will use numpy and sklearn for this. The random number generator is seeded explicitly to enable reproducibility of the experiment.

import numpy as np

import phe as paillier

seed = 42
np.random.seed(seed)

Let's prepare the data first, all wrapped into a function.

def get_data(n_clients):

y = diabetes.target
X = diabetes.data

# Add constant to emulate intercept
X = np.c_[X, np.ones(X.shape[0])]

# The features are already preprocessed
# Shuffle
perm = np.random.permutation(X.shape[0])
X, y = X[perm, :], y[perm]

# Select test at random
test_size = 50
test_idx = np.random.choice(X.shape[0], size=test_size, replace=False)
train_idx = np.ones(X.shape[0], dtype=bool)
train_idx[test_idx] = False
X_test, y_test = X[test_idx, :], y[test_idx]
X_train, y_train = X[train_idx, :], y[train_idx]

# Split train among multiple clients.
# The selection is not at random. We simulate the fact that each client
# sees a potentially very different sample of patients.
X, y = [], []
step = int(X_train.shape[0] / n_clients)
for c in range(n_clients):
X.append(X_train[step * c: step * (c + 1), :])
y.append(y_train[step * c: step * (c + 1)])

return X, y, X_test, y_test

From the learning viewpoint, notice that we are NOT assuming that each hospital sees an unbiased sample from the same patients' distribution: hospitals could be geographically very distant or serve a diverse population. We simulate this condition by sampling patients NOT uniformly at random, but in a biased fashion. The test set is instead an unbiased sample from the overall distribution.

We also define some encrypt/decrypt operations on lists.

def encrypt_vector(pubkey, x):
return [pubkey.encrypt(x[i]) for i in range(x.shape[0])]

def decrypt_vector(privkey, x):
return np.array([privkey.decrypt(i) for i in x])

def sum_encrypted_vectors(x, y):

if len(x) != len(y):
raise Exception('Encrypted vectors must have the same size')

return [x[i] + y[i] for i in range(len(x))]

To evaluate the models, we will compute the mean square error between ground truth and predicted labels.

def mean_square_error(y_pred, y):
return np.mean((y - y_pred) ** 2)
We perform linear regression by gradient descent. The server owns the private key and the clients possess the public key. The protocol works as follows. Until convergence: 1. Hospital 1 computes its gradient, encrypts it and sends it to hospital 2; 2. Hospital 2 computes its gradient, encrypts and sums it to hospital 1's; 3. Hospital 3 does the same and passes the overall sum to the server. 4. The server obtains the gradient of the whole (virtual) training set; it decrypts it and sends it back in the clear to every client, who can update the respective local models. We assume that this aggregate gradient does not disclose any sensitive information about individuals data --- otherwise [differential privacy](https://en.wikipedia.org/wiki/Differential_privacy) could be used on top of our protocol. The next two classes implement the primitives necessary to server and clients for running the protocol.
class Server:
"""Hold the private key. Decrypt the average gradient"""

def __init__(self, key_length=1024):
self.pubkey, self.privkey = \
paillier.generate_paillier_keypair(n_length=key_length)

def decrypt_aggregate(self, input_model, n_clients):
return decrypt_vector(self.privkey, input_model) / n_clients

class Client:
"""Run linear regression either with local data or by gradient steps,
where gradients can be send from remotely.
Hold the private key and can encrypt gradients to send remotely.
"""

def __init__(self, name, X, y, pubkey):
self.name = name
self.pubkey = pubkey
self.X, self.y = X, y
self.weights = np.zeros(X.shape[1])

def fit(self, n_iter, eta=0.01):
"""Linear regression for n_iter"""

for _ in range(n_iter):

"""Update the model with the given gradient"""

"""Return the gradient computed at the current model on all training
set"""

delta = self.predict(self.X) - self.y
return delta.dot(self.X)

def predict(self, X):
"""Score test data"""
return X.dot(self.weights)

When sum_to is given, sum the encrypted gradient to it, assumed
to be another vector of the same size
"""

if sum_to is not None:
raise Exception('Encrypted vectors must have the same size')
else:
return gradient

Now we have all the necessary scaffolding. Let's set up a bunch of parameters and get the data ready.

>>> n_iter, eta = 50, 0.01

>>> names = ['Hospital 1', 'Hospital 2', 'Hospital 3']
>>> n_clients = len(names)
>>>
>>> X, y, X_test, y_test = get_data(n_clients=n_clients)

We instantiate server and clients. Each client gets the public key at creation and its own local dataset.

>>> server = Server(key_length=1024)
>>>
>>> clients = []
>>> for i in range(n_clients):
>>>     clients.append(Client(names[i], X[i], y[i], server.pubkey))

Each client trains a linear regressor on its own data. What is the error (MSE) that each client would get on test set by training only on its own local data?

>>> for c in clients:
>>>     c.fit(n_iter, eta)
>>>     y_pred = c.predict(X_test)
>>>     print('{:s}:\t{:.2f}'.format(c.name, mean_square_error(y_pred, y_test)))
Hospital 1:    3933.78
Hospital 2:    4176.48
Hospital 3:    3795.95

Finally, the federated learning with gradient descent.

>>> for i in range(n_iter):
>>>     # Compute gradients, encrypt and aggregate
>>>     for i in range(1, n_clients):
>>>
>>>     # Send aggregate to server and decrypt it
>>>     aggr = server.decrypt_aggregate(encrypt_aggr, n_clients)
>>>
>>>     for c in clients:
>>>         c.gradient_step(aggr, eta)

What is the error (MSE) that each client gets after running the protocol?

>>> for c in clients:
>>>     y_pred = c.predict(X_test)
>>>     print('{:s}:\t{:.2f}'.format(c.name, mean_square_error(y_pred, y_test)))
Hospital 1:    3695.77
Hospital 2:    3855.14
Hospital 3:    3598.63

As expected, the MSE has decreased for every client. (They are not the same, because the initial model for client was different, i.e. the best model on local data.)

From the security viewpoint, we consider all parties to be "honest but curious". Even by seeing the aggregated gradient in the clear, no participant can pinpoint where patients' data originated. This is true if this RING protocol is run by at least 3 clients, which prevents reconstruction of each others' gradient simply by taking differences.

You can find the code of the full example here. Thanks to the all n1analytics team for feedback and suggestions in writing this post.