基于语言的低级多用途运算引擎安全性

Christian Skalka, Joseph P. Near
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引用次数: 0

摘要

安全多方计算(MPC)是现代分布式应用中数据隐私的一项重要使能技术。目前,低级多方计算协议的证明方法主要是手动的,因此既繁琐又容易出错,而且也是非标准化的,大多数 PL 理论家都不熟悉。为了提供更好的语言支持和基于语言的执行,我们开发了一种新的分阶段 PL,用于定义各种低级概率 MPC 协议。我们还为我们的语言模型制定了一系列信息流中熟悉的保密性和完整性超属性,包括有条件不干涉、逐步释放和稳健解密。我们展示了它们与被动和恶意安全的标准 MPC 威胁模型的关系,以及如何在协议的安全验证中利用它们。为了证明这些特性,我们在$\mathbb{F}_2$中开发了可与分离逻辑式推理相结合的自动策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Language-Based Security for Low-Level MPC
Secure Multi-Party Computation (MPC) is an important enabling technology for data privacy in modern distributed applications. Currently, proof methods for low-level MPC protocols are primarily manual and thus tedious and error-prone, and are also non-standardized and unfamiliar to most PL theorists. As a step towards better language support and language-based enforcement, we develop a new staged PL for defining a variety of low-level probabilistic MPC protocols. We also formulate a collection of confidentiality and integrity hyperproperties for our language model that are familiar from information flow, including conditional noninterference, gradual release, and robust declassification. We demonstrate their relation to standard MPC threat models of passive and malicious security, and how they can be leveraged in security verification of protocols. To prove these properties we develop automated tactics in $\mathbb{F}_2$ that can be integrated with separation logic-style reasoning.
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