# Data Privacy with Homomorphic Encryption in Neural Networks Training and Inference

arXiv: https://arxiv.org/abs/2305.02225 · HTML: https://mlsanity.com/paper/2305.02225/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 3 May 2023.

## Builds on (most cited first, 2 of 2)

- [Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data](https://mlsanity.com/paper/1911.07101/) (2019)
- [Fully Homomorphically Encrypted Deep Learning as a Service](https://mlsanity.com/paper/2107.12997/) (2021)

## Abstract (arXiv)

The use of Neural Networks (NNs) for sensitive data processing is becoming increasingly popular, raising concerns about data privacy and security. Homomorphic Encryption (HE) has the potential to be used as a solution to preserve data privacy in NN. This study provides a comprehensive analysis on the use of HE for NN training and classification, focusing on the techniques and strategies used to enhance data privacy and security. The current state-of-the-art in HE for NNs is analysed, and the challenges and limitations that need to be addressed to make it a reliable and efficient approach for privacy preservation are identified. Also, the different categories of HE schemes and their suitability for NNs are discussed, as well as the techniques used to optimize the accuracy and efficiency of encrypted models. The review reveals that HE has the potential to provide strong data privacy guarantees for NNs, but several challenges need to be addressed, such as limited support for advanced NN operations, scalability issues, and performance trade-offs.