Protocols for secure multi-party private function evaluation

Feras Aljumah, Andrei Soeanu, Wen Liu, M. Debbabi
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引用次数: 2

Abstract

Secure multi-party computation (SMC) allows multiple parties to jointly and securely compute a function while preserving the privacy of the involved parties. In this regard, homomorphic cryptosystems allow users to perform addition or multiplication operations on encrypted values without having to decrypt the input values. In this paper, we propose both a cryptographic and a non-cryptographic privacy-preserving protocols that allow one party to collaboratively compute a private polynomial function with at least two other parties using semantically secure cryptosystems. In this respect, the function to be evaluated is kept private to the party in question. In addition, we assume that there is no fully trusted party and that all parties are semi-honest. Moreover, we assume that there is no collusion between the different parties. We present the two protocols in question and report on the underlying performance through the presentation of our implementation results. The proposed protocols are relevant in many applications such as evidence sharing in multi-party forensic investigations, situation awareness through the secure sharing in distributed monitoring of plan execution, etc.
安全多方私有函数评估协议
安全多方计算(SMC)允许多方共同安全地计算一个函数,同时保护相关各方的隐私。在这方面,同态密码系统允许用户在不解密输入值的情况下对加密值执行加法或乘法操作。在本文中,我们提出了一种加密和一种非加密的隐私保护协议,该协议允许一方使用语义安全的密码系统与至少两个其他方协作计算私有多项式函数。在这方面,要评估的函数对相关方是保密的。此外,我们假设没有完全可信的一方,所有各方都是半诚实的。此外,我们假定各方之间不存在勾结。我们介绍了所讨论的两个协议,并通过介绍我们的实现结果来报告其底层性能。所提出的协议适用于多方取证调查中的证据共享、计划执行分布式监控中通过安全共享实现的态势感知等应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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