Privacy-Preserving Epidemiology Study for Vertically Partitioned Datasets

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Bloom过滤器（BF）的内积，所提出的方案在beta分布的假设下应用贝叶斯估计来估计大小的先验概率。该协议在保持通信复杂性的基础上，利用贝叶斯估计提高了估计精度，并以幽门螺杆菌感染和胃癌两个属性的流行病学数据为例进行了验证。假设与幽门螺杆菌感染和胃癌相关的信息是分开收集的，该方案表明，卡方检验可以在不披露两个机密数据库内容的情况下进行。

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.