Paper 2022/202
Through the Looking-Glass: Benchmarking Secure Multi-Party Computation Comparisons for ReLU's
, Technology Innovation Institute, Technology Innovation Institute, Technology Innovation Institute, Technology Innovation InstituteAbstract
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.
Note: Less typos and grammar corrected. Camera Ready Version
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Published elsewhere. CANS 2022
- Keywords
- Secure Multiparty Computation Privacy Preserving Machine Learning Applied Cryptography ReLU Functions
- Contact author(s)
-
Abdelrahaman aly @ gmail com
Kashif nawaz @ tii ae
eugenio salazar @ tii ae
victor sucasas @ tii ae - History
- 2022-09-15: last of 5 revisions
- 2022-02-20: received
- See all versions
- Short URL
- https://ia.cr/2022/202
- License
-
CC BY
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}
}
