Tetrad: Actively Secure 4PC for Secure Training and Inference
This work addresses the need for efficient and secure machine learning training and inference in scenarios involving multiple parties, offering incremental improvements over existing methods.
The paper tackles the problem of secure multi-party computation for privacy-preserving machine learning by proposing Tetrad, a framework for four parties with at most one active corruption, which improves training speed by up to 4 times and inference speed by up to 5 times compared to prior work.
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.