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 of magnitude, however the communication between the proxy and cloud is still excessive. Second, to we develop an optimized garbled circuit that leverages truncated secret shares for faster evaluation and less proxy-cloud communication. Finally, we propose sparse HE convolution to reduce the computational bottleneck of using HE. Compared to the state-of-the-art, these optimizations provide a bandwidth savings of over 3X and speedup of 4X for private deep learning inference.
翻译:本文建议采用Impala, 这是一种用于客户- cloud 环境中私人推断的新的加密协议。 Impala 以最新解决方案为基础,这些解决方案结合了同质加密(HE)和安全多方计算(MPC)的互补优势。 制定了一系列协议优化办法,以减少通信和性能瓶颈。 首先,我们通过引入代理服务器和开发低管钥匙转换技术,从客户中消除了MPC极高的通信成本。 键转换将客户带宽减少多个数量级, 但代理和云层之间的通信仍然过量。 其次,我们开发了一种优化的电路,利用秘密股份进行快速评估和减少代理- 库通信。 最后,我们建议稀有电动能减少使用HE的计算瓶颈。 与最新技术相比,这些优化可以节省超过3X的带宽和加速4X的频段, 用于私人深层学习。