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 6x over Trident across these