Paper 2022/202 Original: eprint.iacr.org/2022/202 ↗
Through the Looking-Glass: Benchmarking Secure Multi-Party Computation Comparisons for ReLU's
Abstract
Comparisons or Inequality Tests are an essential building block of Rectified Linear Unit functions (ReLU's), ever more present in Machine Learning, specifically in Neural Networks. Motivated by the increasing interest in privacy-preserving Artificial Intelligence, we explore the current state of the art of privacy preserving comparisons over Multi-Party Computation (MPC). We then introduce constant round variations and combinations, which are compatible with customary fixed point arithmetic over MPC. Our main focus is implementation and benchmarking; hence, we showcase our contributions via an open source library, compatible with current MPC software tools. Furthermore, we include a comprehensive comparative analysis on various adversarial settings. Our results improve running times in practical scenarios. Finally, we offer conclusions about the viability of these protocols when adopted for privacy-preserving Machine Learning.
Metadata
- Available format(s)
PDF- Category
- Applications
- Publication info
- Preprint.
- History
- 2022-09-15: last updated
- 2022-02-20: published
- Short URL
- https://ia.cr/2022/202
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2022/202,
author = {Abdelrahaman Aly and Kashif Nawaz and Eugenio Salazar and Victor Sucasas},
title = {Through the Looking-Glass: Benchmarking Secure Multi-Party Computation Comparisons for ReLU's},
howpublished = {Cryptology {ePrint} Archive, Paper 2022/202},
year = {2022},
url = {https://eprint.iacr.org/2022/202}
}