Paper 2021/755
Tetrad: Actively Secure 4PC for Secure Training and Inference
Nishat Koti and Arpita Patra and Rahul Rachuri and Ajith Suresh
Abstract
In this work, we design an efficient mixed-protocol framework, Tetrad, with applications to privacy-preserving machine learning. It is designed for the four-party setting with at most one active corruption and supports rings. Our fair multiplication protocol requires communicating only $5$ ring elements improving over the state-of-the-art protocol of Trident (Chaudhari et al. NDSS'20). The technical highlights of Tetrad include efficient (a) truncation without any overhead, (b) multi-input multiplication protocols for arithmetic and boolean worlds, (c) garbled-world, tailor-made for the mixed-protocol framework, and (d) conversion mechanisms to switch between the computation styles. The fair framework is also extended to provide robustness without inflating the costs. The competence of Tetrad is tested with benchmarks for deep neural networks such as LeNet and VGG16 and support vector machines. One variant of our framework aims at minimizing the execution time, while the other focuses on the monetary cost. We observe improvements up to $6\times$ over Trident across these parameters.
Metadata
- Available format(s)
- Category
- Cryptographic protocols
- Publication info
- Preprint. MINOR revision.
- Keywords
- 4PCfairrobustmulti-party computationprivacy preserving machine learning
- Contact author(s)
- kotis @ iisc ac in,arpita @ iisc ac in,rachuri @ cs au dk,ajith @ iisc ac in
- History
- 2022-01-03: last of 3 revisions
- 2021-06-07: received
- See all versions
- Short URL
- https://ia.cr/2021/755
- License
-
CC BY