# Private Machine Learning in TensorFlow using Secure Computation

arXiv: https://arxiv.org/abs/1810.08130 · HTML: https://mlsanity.com/paper/1810.08130/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2018.

## Built on by (most cited first, 15 of 15)

- [Secure Quantized Training for Deep Learning](https://mlsanity.com/paper/2107.00501/) (2021)
- [Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy](https://mlsanity.com/paper/2208.08662/) (2022)
- [Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications](https://mlsanity.com/paper/2506.00660/) (2025)
- [Enabling Homomorphically Encrypted Inference for Large DNN Models](https://mlsanity.com/paper/2103.16139/) (2021)
- [Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification](https://mlsanity.com/paper/2111.14838/) (2021)
- [Hijack Vertical Federated Learning Models As One Party](https://mlsanity.com/paper/2212.00322/) (2022)
- [Efficient ML Models for Practical Secure Inference](https://mlsanity.com/paper/2209.00411/) (2022)
- [XAI for time-series classification leveraging image highlight methods](https://mlsanity.com/paper/2311.17110/) (2023)
- [Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training](https://mlsanity.com/paper/2403.03592/) (2024)
- [Efficient CNN Building Blocks for Encrypted Data](https://mlsanity.com/paper/2102.00319/) (2021)
- [Privacy-preserving Machine Learning for Medical Image Classification](https://mlsanity.com/paper/2108.12816/) (2021)
- [Covert Attacks on Machine Learning Training in Passively Secure MPC](https://mlsanity.com/paper/2505.17092/) (2025)
- [Morse-STF: Improved Protocols for Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2109.11726/) (2021)
- [Towards General-purpose Infrastructure for Protecting Scientific Data Under Study](https://mlsanity.com/paper/2110.01315/) (2021)
- [MPC-Patch-Bench: Security-Aware LLM Code Patch for Multi-Party Computation](https://mlsanity.com/paper/2606.11416/) (2026)

## Abstract (arXiv)

We present a framework for experimenting with secure multi-party computation directly in TensorFlow. By doing so we benefit from several properties valuable to both researchers and practitioners, including tight integration with ordinary machine learning processes, existing optimizations for distributed computation in TensorFlow, high-level abstractions for expressing complex algorithms and protocols, and an expanded set of familiar tooling. We give an open source implementation of a state-of-the-art protocol and report on concrete benchmarks using typical models from private machine learning.