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TF Encrypted is a framework for encrypted machine learning in TensorFlow. It looks and feels like TensorFlow, taking advantage of the ease-of-use of the Keras API while enabling training and prediction over encrypted data via secure multi-party computation and homomorphic encryption. TF Encrypted aims to make privacy-preserving machine learning readily available, without requiring expertise in cryptography, distributed systems, or high performance computing.

See below for more background material, explore the examples, or visit the documentation to learn more about how to use the library.

Website Documentation PyPI CircleCI Badge

Now, TF Encrypted is based on tensorflow 2 !

TF1 execute computation by building a graph first, then run this graph in a session. This is hard to use for a lot of developers, especially for researchers not major in computer science. Therefore, TF1 has very few users and is not maintained any more. Since TF Encrypted based on TF1, it face the same problem that TF1 has encountered. So we update TF Encrypted, to rely on TF2 to support eager execution which makes development on TF Encrypted more easier. At the same time, it also supports building graph implicitly by tfe.function to realize nearly the same performance as TF Encrypted based on TF1. Unfortunately, after updated, TF1 features like session and placeholder are not supported by TFE any more. For those developers who want to use TF1 like TFE, we suggest them to use version 0.8.0.


TF Encrypted is available as a package on PyPI supporting Python 3.8+ and TensorFlow 2.9.1+:

pip install tf-encrypted

Creating a conda environment to run TF Encrypted code can be done using:

conda create -n tfe python=3.8
conda activate tfe
conda install tensorflow notebook
pip install tf-encrypted

Alternatively, installing from source can be done using:

git clone
cd tf-encrypted
pip install -e .
make build

This latter is useful on platforms for which the pip package has not yet been compiled but is also needed for development. Note that this will get you a working basic installation, yet a few more steps are required to match the performance and security of the version shipped in the pip package, see the installation instructions.


The following is an example of simple matmul on encrypted data using TF Encrypted based on TF2, you could execute matmul in eager mode or building a graph by @tfe.function.

import tensorflow as tf
import tf_encrypted as tfe

def provide_input():
    # normal TensorFlow operations can be run locally
    # as part of defining a private input, in this
    # case on the machine of the input provider
    return tf.ones(shape=(5, 10))

# provide inputs
w = tfe.define_private_variable(tf.ones(shape=(10,10)))
x = provide_input()

# eager execution
y = tfe.matmul(x, w)
res = y.reveal().to_native()

# build graph and run graph
def matmul_func(x, w)
    y = tfe.matmul(x, w)
    return y.reveal().to_native()

res = matmul_func(x, w)

For more information, check out the documentation or the examples.


All tests are performed by using the ABY3 protocol among 3 machines, each with 4 cores (Intel Xeon Platinum 8369B CPU @ 2.70GHz), Ubuntu 18.04 (64bit), TensorFlow 2.9.1, Python 3.8.13 and pip 21.1.2. The LAN environment has a bandwidth of 40 Gbps and a RTT of 0.02 ms, and the WAN environment has a bandwidth of 352 Mbps and a RTT of 40 ms.

You can find source code of the following benchmarks in ./examples/benchmark/ and corresponding guidelines of how to reproduce them.

Benchmark 1: Sort and Max

Graph building is a one-time cost, while LAN or WAN timings are average running time across multiple runs. For example, it takes 152.5 seconds to build the graph for Resnet50 model, and afterwards, it only takes 19.1 seconds to predict each image.

Build graph
Sort/Max (2^10)1 5.26 0.14 10.37
Sort/Max (2^16)1 14.06 7.37 41.97
Max (2^10 $\times$ 4)2 5.63 0.01 0.55
Max (2^16 $\times$ 4)2 5.81 0.29 1.14

1 Max is implemented by using a sorting network, hence its performance is essentially the same as Sort. Sorting network can be efficiently constructed as a TF graph. The traditional way of computing Max by using a binary comparison tree does not work well in a TF graph, because the graph becomes huge when the number of elements is large.

2 This means 2^10 (respectively, 2^16) invocations of max on 4 elements, which is essentially a MaxPool with pool size of 2 x 2.

Benchmark 2: Neural Network Inference

We show the strength of TFE by loading big TF models (e.g. RESNET50) and run private inference on top of it.

Build graph
VGG19 inference time (seconds) 105.18 49.63 139.63
RESNET50 inference time (seconds) 150.47 19.071 84.29
DENSENET121 inference time (seconds) 344.55 33.53 151.43

1 This is currently one of the fastest implementation of secure RESNET50 inference (three-party). Comparable with CryptGPU , SecureQ8, and faster than CryptFLOW.

Benchmark 3: Neural Network Training

We benchmark the performance of training several neural network models on the MNIST dataset (60k training images, 10k test images, and batch size is 128). The definitions of these models can be found in examples/models.

Accuracy (epochs) Accuracy (epochs) Seconds per Batch (LAN) Seconds per Batch (LAN) Seconds per Batch (WAN) Seconds per Batch (WAN)
A (SGD) 96.7% (5) 96.5% (5) 0.098 0.167 9.724 4.510
A (AMSgrad) 97.8% (5) 97.4% (5) 0.228 0.717 21.038 15.472
A (Adam ) 97.4% (5) 97.4% (5) 0.221 0.535 50.963 15.153
B (SGD) 97.5% (5) 98.6% (5) 0.571 5.332 60.755 18.656
B (AMSgrad) 98.6% (5) 98.7% (5) 0.680 5.722 71.983 21.647
B (Adam) 98.8% (5) 98.8% (5) 0.772 5.733 98.108 21.130
C (SGD) 98.5% (5) 98.7% (5) 1.175 8.198 91.341 27.102
C (AMSgrad) 98.9% (5) 98.9% (5) 1.568 10.053 119.271 66.357
C (Adam) 99.0% (5) 99.1% (5) 2.825 9.893 195.013 65.320
D (SGD) 97.6% (5) 97.1% (5) 0.134 0.439 15.083 5.465
D (AMSgrad) 98.4% (5) 97.4% (5) 0.228 0.900 26.099 14.693
D (Adam) 98.2% (5) 97.6% (5) 0.293 0.710 54.404 14.515

We also give the performance of training a logistic regression model in the following table. This model is trained to classify two classes: small digits (0-4) vs large digits (5-9). Dataset can be found in examples/benchmark/training/

Accuracy (epochs) Seconds per Batch (LAN) Seconds per Batch (WAN)
LR (SGD) 84.8% (5) 0.010 0.844
LR (AMSgrad) 85.0% (5) 0.023 1.430
LR (Adam) 85.2% (5) 0.019 1.296


  • High-level APIs for combining privacy and machine learning. So far TF Encrypted is focused on its low-level interface but it's time to figure out what it means for interfaces such as Keras when privacy enters the picture.

  • Tighter integration with TensorFlow. This includes aligning with the upcoming TensorFlow 2.0 as well as figuring out how TF Encrypted can work closely together with related projects such as TF Privacy and TF Federated.

  • Support for third party libraries. While TF Encrypted has its own implementations of secure computation, there are other excellent libraries out there for both secure computation and homomorphic encryption. We want to bring these on board and provide a bridge from TensorFlow.

Background & Further Reading

Blog posts:




Development and Contribution

TF Encrypted is open source community project developed under the Apache 2 license and maintained by a set of core developers. We welcome contributions from all individuals and organizations, with further information available in our contribution guide. We invite any organizations interested in partnering with us to reach out via email.

Don't hesitate to send a pull request, open an issue, or ask for help! We use ZenHub to plan and track GitHub issues and pull requests.

Individual contributions

We appreciate the efforts of all contributors that have helped make TF Encrypted what it is! Below is a small selection of these, generated by from most recent stats:

Organizational contributions

We are very grateful for the significant contributions made by the following organizations!

Cape Privacy Alibaba Security Group OpenMined

Project Status

TF Encrypted is experimental software not currently intended for use in production environments. The focus is on building the underlying primitives and techniques, with some practical security issues postponed for a later stage. However, care is taken to ensure that none of these represent fundamental issues that cannot be fixed as needed.

Known limitations

  • Elements of TensorFlow's networking subsystem does not appear to be sufficiently hardened against malicious users. Proxies or other means of access filtering may be sufficient to mitigate this.


Please open an issue, or send an email to [email protected].


Licensed under Apache License, Version 2.0 (see LICENSE or Copyright as specified in NOTICE.

TF Encrypted's Projects

aes-prng icon aes-prng

Rust pseudo-random number generator based on AES

community icon community

Stores documents used by the TensorFlow developer community

federated icon federated

A framework for implementing federated learning

moose icon moose

Secure distributed dataflow framework for encrypted machine learning and data processing

rfcs icon rfcs

Community design documents

tf-big icon tf-big

Arbitrary precision integers in TensorFlow

tf-encrypted icon tf-encrypted

A Framework for Encrypted Machine Learning in TensorFlow

tf-pjc icon tf-pjc

Bridge between TensorFlow and Google's Private Join and Compute library

tf-seal icon tf-seal

Bridge between TensorFlow and the Microsoft SEAL homomorphic encryption library

vkquake icon vkquake

Vulkan Quake port based on QuakeSpasm

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