Tetrad: Actively Secure 4PC for Secure Training and Inference

TL;DR

Tetrad is a new four-party secure computation framework that improves efficiency and supports active security for privacy-preserving machine learning, enabling faster training and inference with lower deployment costs.

Contribution

Tetrad introduces a novel actively secure 4-party protocol with improved multiplication efficiency and integrated robustness, supporting mixed arithmetic and boolean circuits for machine learning tasks.

Findings

Tetrad achieves up to 4x faster training and 5x faster inference.

It reduces deployment costs by up to 6x compared to previous protocols.

Supports probabilistic truncation and domain conversion without overhead.

Abstract

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.