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Bloom Filter Bootstrap: Privacy-Preserving Estimation of the Size of an Intersection

机译:Bloom Filter Bootstrap:交叉口大小的隐私保护估计

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This paper proposes a new privacy-preserving scheme for estimating the size of the intersection of two given secret subsets. Given the inner product of two Bloom filters (BFs) of the given sets, the proposed scheme applies Bayesian estimation under assumption of beta distribution for an a priori probability of the size to be estimated. The BF retains the communication complexity and the Bayesian estimation improves the estimation accuracy. An possible application of the proposed protocol is an epidemiologi-cal datasets regarding two attributes, Helicobactor pylori infection and stomach cancer. Assuming information related to Helicobactor Pylori infection and stomach cancer are separately collected, the protocol demonstrates that a x~2-test can be performed without disclosing the contents of the two confidential databases.
机译:本文提出了一种新的隐私保护方案,用于估计两个给定秘密子集的交集的大小。给定给定集合的两个布隆过滤器(BF)的内积,对于要估计的大小的先验概率,所提出的方案在贝塔分布的假设下应用贝叶斯估计。 BF保留了通信复杂性,贝叶斯估计提高了估计准确性。提议的协议的可能应用是关于两个属性(幽门螺杆菌感染和胃癌)的流行病学数据集。假设分别收集了与幽门螺杆菌感染和胃癌有关的信息,则该协议表明可以进行x〜2-test,而无需透露两个机密数据库的内容。

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