Privacy-Preserving Epidemiology Study for Vertically Partitioned Datasets

本文提出了一种新的估计两个给定秘密子集相交大小的隐私保护方案。给定给定集合的两个Bloom过滤器（bf）的内积，该方案在β分布假设下对待估计大小的先验概率进行贝叶斯估计。BF保持了通信复杂度，贝叶斯估计提高了估计精度。该方案的一个可能应用是关于幽门螺杆菌感染和胃癌两个属性的流行病学数据集。假设单独收集幽门螺杆菌感染和胃癌的相关信息，方案表明可以在不泄露两个保密数据库内容的情况下进行卡方检验。

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 an 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.A possible application of the proposed protocol is an epidemiological datasets regarding two attributes, Helicobacter pylori infection and stomach cancer. Assuming information related to Helicobacter Pylori infection and stomach cancer are separately collected, the protocol demonstrates that a Chi-squared test can be performed without disclosing the contents of the two confidential databases.