论文标题

多方私人集交叉点:信息理论方法

Multi-Party Private Set Intersection: An Information-Theoretic Approach

论文作者

Wang, Zhusheng, Banawan, Karim, Ulukus, Sennur

论文摘要

我们研究了多方私人集交叉路口(MP-PSI)的问题。 In MP-PSI, there are $M$ parties, each storing a data set $\mathcal{p}_i$ over $N_i$ replicated and non-colluding databases, and we want to calculate the intersection of the data sets $\cap_{i=1}^M \mathcal{p}_i$ without leaking any information beyond the set intersection to any of the parties.我们考虑一个特定的通信协议,其中一个称为领导者的当事方通过向其余的当事方发送查询,从而启动MP-PSI协议,称为客户当事方。客户政党不允许彼此交流。我们提出了一个信息理论方案,该方案私下计算交叉点$ \ cap_ {i = 1}^m \ mathcal {p} _i $,下载成本为$ d = \ min_ {t \ min_ {t \ in \ in \ in \ {1,\ cdots,\ cdots,m \}}}}}}}}}}}}}}} {t}} \ left \ lceil \ frac {| \ mathcal {p} _t | n_i} {n_i-birceil \ right \ rceil $。与2方PSI问题类似,我们的方案建立在PSI问题与多消息对称的私人信息检索(MM-SPIR)问题之间的基础上。我们的方案是对2方PSI方案的非平凡概括,因为它需要对共享的共同随机性进行复杂的设计。有趣的是,就下载成本而言,由于MP-PSI问题在MP-PSI问题中与2派对PSI问题相比,我们的计划不会受到任何罚款。

We investigate the problem of multi-party private set intersection (MP-PSI). In MP-PSI, there are $M$ parties, each storing a data set $\mathcal{p}_i$ over $N_i$ replicated and non-colluding databases, and we want to calculate the intersection of the data sets $\cap_{i=1}^M \mathcal{p}_i$ without leaking any information beyond the set intersection to any of the parties. We consider a specific communication protocol where one of the parties, called the leader party, initiates the MP-PSI protocol by sending queries to the remaining parties which are called client parties. The client parties are not allowed to communicate with each other. We propose an information-theoretic scheme that privately calculates the intersection $\cap_{i=1}^M \mathcal{p}_i$ with a download cost of $D = \min_{t \in \{1, \cdots, M\}} \sum_{i \in \{1, \cdots M\}\setminus {t}} \left\lceil \frac{|\mathcal{p}_t|N_i}{N_i-1}\right\rceil$. Similar to the 2-party PSI problem, our scheme builds on the connection between the PSI problem and the multi-message symmetric private information retrieval (MM-SPIR) problem. Our scheme is a non-trivial generalization of the 2-party PSI scheme as it needs an intricate design of the shared common randomness. Interestingly, in terms of the download cost, our scheme does not incur any penalty due to the more stringent privacy constraints in the MP-PSI problem compared to the 2-party PSI problem.

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