Paper 2023/1890
Aegis: A Lightning Fast Privacy-preserving Machine Learning Platform against Malicious Adversaries
Abstract
Privacy-preserving machine learning (PPML) techniques have gained significant popularity in the past years. Those protocols have been widely adopted in many real-world security-sensitive machine learning scenarios, e.g., medical care and finance. In this work, we introduce $\mathsf{Aegis}$~-- a high-performance PPML platform built on top of a maliciously secure 3-PC framework over ring $\mathbb{Z}_{2^\ell}$. In particular, we propose a novel 2-round secure comparison (a.k.a., sign bit extraction) protocol in the preprocessing model. The communication of its semi-honest version is only 25% of the state-of-the-art (SOTA) constant-round semi-honest comparison protocol by Zhou et al. (S&P 2023); communication and round complexity of its malicious version are approximately 25% and 50% of the SOTA (BLAZE) by Patra and Suresh (NDSS 2020), for $\ell=64$. Moreover, the overall communication of our maliciously secure inner product protocol is merely $3\ell$ bits, reducing 50% from the SOTA (Swift) by Koti et al. (USENIX 2021). Finally, the resulting ReLU and MaxPool PPML protocols outperform the SOTA constructions by $4\times$ in the semi-honest setting and $100\times$ in the malicious setting, respectively.
Metadata
- Available format(s)
- Category
- Cryptographic protocols
- Publication info
- Preprint.
- Contact author(s)
- lutianpei @ zju edu cn
- History
- 2024-05-29: last of 2 revisions
- 2023-12-08: received
- See all versions
- Short URL
- https://ia.cr/2023/1890
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2023/1890, author = {Tianpei Lu and Bingsheng Zhang and Lichun Li and Kui Ren}, title = {Aegis: A Lightning Fast Privacy-preserving Machine Learning Platform against Malicious Adversaries}, howpublished = {Cryptology {ePrint} Archive, Paper 2023/1890}, year = {2023}, url = {https://eprint.iacr.org/2023/1890} }