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Impala: Low-Latency, Communication-Efficient Private Deep Learning Inference. (arXiv:2205.06437v1 [cs.CR])
May 16, 2022, 1:20 a.m. | Woo-Seok Choi, Brandon Reagen, Gu-Yeon Wei, David Brooks
cs.CR updates on arXiv.org arxiv.org
This paper proposes Impala, a new cryptographic protocol for private
inference in the client-cloud setting. Impala builds upon recent solutions that
combine the complementary strengths of homomorphic encryption (HE) and secure
multi-party computation (MPC). A series of protocol optimizations are developed
to reduce both communication and performance bottlenecks. First, we remove
MPC's overwhelmingly high communication cost from the client by introducing a
proxy server and developing a low-overhead key switching technique. Key
switching reduces the clients bandwidth by multiple orders …
More from arxiv.org / cs.CR updates on arXiv.org
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