Tetrad: Actively Secure 4PC for Secure Training and Inference

Nishat Koti, Ajith Suresh, Sai Rahul Rachuri, Arpita Patra

Research output: Contribution to conferencePaperResearch


Mixing arithmetic and boolean circuits to perform privacy-preserving machine learning has become increasingly popular. Towards this, we propose a framework for the case of four parties with at most one active corruption called Tetrad. Tetrad works over rings and supports two levels of security, fairness and robustness. The fair multiplication protocol costs 5 ring elements, improving over the state-of-the-art Trident (Chaudhari et al. NDSS'20). A key feature of Tetrad is that robustness comes for free over fair protocols. Other highlights across the two variants include (a) probabilistic truncation without overhead, (b) multi-input multiplication protocols, and (c) conversion protocols to switch between the computational domains, along with a tailor-made garbled circuit approach. Benchmarking of Tetrad for both training and inference is conducted over deep neural networks such as LeNet and VGG16. We found that Tetrad is up to 4 times faster in ML training and up to 5 times faster in ML inference. Tetrad is also lightweight in terms of deployment cost, costing up to 6 times less than Trident.

Original languageEnglish
Publication date2022
Number of pages18
Publication statusPublished - 2022
EventNetwork and Distributed Systems Security (NDSS) Symposium 2022 - San Diego ,Calif., United States
Duration: 24 Apr 202228 Apr 2022


ConferenceNetwork and Distributed Systems Security (NDSS) Symposium 2022
Country/TerritoryUnited States
CitySan Diego ,Calif.


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