基于对称密钥技术的实用多方私集交集

V. Kolesnikov, Naor Matania, Benny Pinkas, Mike Rosulek, Ni Trieu
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引用次数: 150

摘要

我们提出了一个多方私有集交集(PSI)的新范式,它允许$n$各方在不透露任何额外信息的情况下计算其数据集的交集。我们探索了这种范式的各种实例。我们的协议避免了计算上昂贵的公钥操作,并且在任何数量的半诚实参与者(即没有诚实多数)存在的情况下都是安全的。我们用一个实现来证明我们协议的实用性。据我们所知,这是第一次实施多方防扩散倡议协议。对于5个拥有220项数据集的各方,我们的协议只需要72秒。在实现稍弱的安全性变体(增强半诚实模型)的优化中,相同的任务只需要22秒。我们协议的技术核心是可编程伪随机函数(OPPRF)的遗忘评估,我们以三种不同的方式实例化了它。我们相信我们的新的OPPRF抽象和构造可能是独立的兴趣。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Practical Multi-party Private Set Intersection from Symmetric-Key Techniques
We present a new paradigm for multi-party private set intersection (PSI) that allows $n$ parties to compute the intersection of their datasets without revealing any additional information. We explore a variety of instantiations of this paradigm. Our protocols avoid computationally expensive public-key operations and are secure in the presence of any number of semi-honest participants (i.e., without an honest majority). We demonstrate the practicality of our protocols with an implementation. To the best of our knowledge, this is the first implementation of a multi-party PSI protocol. For 5 parties with data-sets of 220 items each, our protocol requires only 72 seconds. In an optimization achieving a slightly weaker variant of security (augmented semi-honest model), the same task requires only 22 seconds. The technical core of our protocol is oblivious evaluation of a programmable pseudorandom function (OPPRF), which we instantiate in three different ways. We believe our new OPPRF abstraction and constructions may be of independent interest.
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