论文标题

参议院:合作分析的恶意安全MPC平台

Senate: A Maliciously-Secure MPC Platform for Collaborative Analytics

论文作者

Poddar, Rishabh, Kalra, Sukrit, Yanai, Avishay, Deng, Ryan, Popa, Raluca Ada, Hellerstein, Joseph M.

论文摘要

许多组织将从将数据汇集在一起​​中受益,以便吸引互惠互利的见解 - 例如,对于跨银行的欺诈检测,跨医院的更好的医学研究等。但是,通常会阻止此类组织通过隐私问题,监管障碍或商业竞争来互相共享数据。我们提出参议院,该系统允许多方协作运行分析SQL查询,而无需互相揭示其各个数据。与以前的安全多方计算(MPC)的工作不同,假设所有各方都是半honest,参议院即使在存在恶意对手的情况下也保护数据。参议院的核心是一种新的MPC分解协议,该协议将加密MPC计算分解为较小的单位,其中一些可以由当事方的子集和并行执行,同时保留其安全保证。参议院随后提供了一种新的查询计划算法,该算法有效地分解和计划了加密计算,其性能比最先进的速度快145美元。

Many organizations stand to benefit from pooling their data together in order to draw mutually beneficial insights -- e.g., for fraud detection across banks, better medical studies across hospitals, etc. However, such organizations are often prevented from sharing their data with each other by privacy concerns, regulatory hurdles, or business competition. We present Senate, a system that allows multiple parties to collaboratively run analytical SQL queries without revealing their individual data to each other. Unlike prior works on secure multi-party computation (MPC) that assume that all parties are semi-honest, Senate protects the data even in the presence of malicious adversaries. At the heart of Senate lies a new MPC decomposition protocol that decomposes the cryptographic MPC computation into smaller units, some of which can be executed by subsets of parties and in parallel, while preserving its security guarantees. Senate then provides a new query planning algorithm that decomposes and plans the cryptographic computation effectively, achieving a performance of up to 145$\times$ faster than the state-of-the-art.

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