Affiliations: College of Computer Science and Engineering, Northeastern University, Shenyang, China
Correspondence:
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Corresponding author. Derong Shen, College of Computer Science and Engineering, Northeastern University, Shenyang 110167, China. E-mail: shenderong@ise.neu.edu.cn.
Abstract: Matching similar records from different databases to prevent duplication in a private manner has attracted plenty of attention, which is referred to as Private Entity Matching (PEM). In spite of various approaches having been proposed to solve this problem, private linking numerical data such as integer (e.g. age), floating point (e.g. body mass index) from multiple databases is an urgent gap, which is commonly required in health domain, statistical departments and more. Hence, this paper targets at solving the problem of linking numerical data from three or more sources in an efficient and secure way. Firstly, we introduce a novel homomorphic encryption method constrained similar modul, which provides strong privacy to encrypt numerical data in the range of real numbers. Then, to avoid frequent decryptions in the homomorphic encryption schema, we draw an inference about the encryption keys. Finally, an accelerated algorithm is proposed to reduce the complexity of multi-party numerical records matching. Our approach is considered absolute safety that no party learns any sensitive information of the others in the absence of collusion. Experiments on two real-world health information databases of patient records validate our approach with regards to the efficiency improvement and at the same time, at no the sacrifice of linkage quality.
