# A generic framework for privacy preserving deep learning

arXiv: https://arxiv.org/abs/1811.04017 · HTML: https://mlsanity.com/paper/1811.04017/

## Key facts

- Cited by 103 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2018.

## Built on by (most cited first, 30 of 103)

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- [Secure Quantized Training for Deep Learning](https://mlsanity.com/paper/2107.00501/) (2021)
- [FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning](https://mlsanity.com/paper/2109.00675/) (2021)
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- [Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2101.03655/) (2021)
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## Abstract (arXiv)

We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors. This abstraction allows one to implement complex privacy preserving constructs such as Federated Learning, Secure Multiparty Computation, and Differential Privacy while still exposing a familiar deep learning API to the end-user. We report early results on the Boston Housing and Pima Indian Diabetes datasets. While the privacy features apart from Differential Privacy do not impact the prediction accuracy, the current implementation of the framework introduces a significant overhead in performance, which will be addressed at a later stage of the development. We believe this work is an important milestone introducing the first reliable, general framework for privacy preserving deep learning.