Getting Started

This walkthrough assumes that you have installed TF Encrypted by following the installation instructions.

TF Encrypted is a secure multiparty computation library where multiple people (or “parties”) work together to compute results in a secure fashion without any one party having access to the underlying data. This is achieved by splitting up the input data into shares that are perfectly secure.

Introduction to TF Encrypted’s API

TF Encrypted provides an API similar to TensorFlow that data scientists and researchers can use to train models and predict upon them in privacy-preserving fashion.

One of the goals of TF Encrypted is to make experimenting with secure private machine learning accessible to anyone. To do this, we’ve implemented an API that is very similar to TensorFlow while abstracting away the complexity of securely managing public and private data. The PondTensor is the primary abstraction provided for managing public and private data.

The following example demonstrates constructing a public value (known to all parties) using tfe.define_public_variable.

import numpy as np
import tf_encrypted as tfe

variable = tfe.define_public_variable(np.array([1,2,3]))
print(variable) # PondPublicVariable(shape=(3,))

We can then perform operations on these Tensors which define an underlying computation graph which can be executed inside a Session which manages figuring out which nodes run which parts of the computation. This is demonstrated in the following example:

variable = tfe.define_public_variable(np.array([1,2,3]))
answer = variable * 2

sess = tfe.Session(), tag='init') # ignore this for now :)

# => array([2., 4., 6.])

Similar to public variables we can define private variables as demonstrated below:

variable = tfe.define_private_variable(np.array([1,2,3]))

sess = tfe.Session(), tag='init')[variable.share0, variable.share1])

# => [array([ 1601115100, -2072569751,  -600438257], dtype=int32),
#     array([-1601049564,  2072700823,   600634865], dtype=int32)]

Unlike with public tensors, each node involved in a computation will get a different share of the encrypted (private) data. This sharing mechanism is the backbone of multiparty computation.

For more in depth examples of how to use TF Encrypted to train and predict upon machine learning models please check out our MNIST or Logistic Regression guides.

If you have any questions, please don’t hesitate to reach out via a GitHub Issue.