面向知识的安全多方计算

Piotr (Peter) Mardziel, M. Hicks, Jonathan Katz, M. Srivatsa
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引用次数: 19

摘要

用于安全多方计算(SMC)的协议允许一组互不信任的各方计算其私有输入的函数f,而除了结果所暗示的内容外,不透露任何有关其输入的信息。然而,根据f的不同,结果本身可能会透露出各方不满意的更多信息。以前几乎所有关于SMC的工作都把f当作给定的。没有回答的问题是,各方应该如何决定他们首先计算f是否“安全”。为了解决这个问题,我们提出了一种将信念跟踪应用于SMC的方法。在我们的方法中,每个参与方都能够推断出其他参与方由于计算f而可能获得的知识增长,并且可以选择不参与(或仅部分参与)以限制知识的增长。我们开发了两种技术-信念集方法和SMC信念跟踪方法-证明它们是合理的,并通过一系列实验讨论了它们的精度/性能权衡。
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Knowledge-oriented secure multiparty computation
Protocols for secure multiparty computation (SMC) allow a set of mutually distrusting parties to compute a function f of their private inputs while revealing nothing about their inputs beyond what is implied by the result. Depending on f, however, the result itself may reveal more information than parties are comfortable with. Almost all previous work on SMC treats f as given. Left unanswered is the question of how parties should decide whether it is "safe" for them to compute f in the first place. We propose here a way to apply belief tracking to SMC in order to address exactly this question. In our approach, each participating party is able to reason about the increase in knowledge that other parties could gain as a result of computing f, and may choose not to participate (or participate only partially) so as to restrict that gain in knowledge. We develop two techniques---the belief set method and the SMC belief tracking method---prove them sound, and discuss their precision/performance tradeoffs using a series of experiments.
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