Scalable Private Blocking Technique for Privacy-Preserving Record Linkage

Abstract

Record linkage is the process of matching records from multiple databases that refer to the same entities and it has become an increasingly important subject in many application areas, including business, government, and health. When we collect data which is about people from these areas, integrating such data across organizations can raise privacy concerns. To prevent privacy breaches, ideally records should be linked in a private way such that no information other than the matching result is leaked in the process, this technique is called privacy-preserving record linkage (PPRL). Scalability is one of the main challenges in PPRL, therefore, many private blocking techniques have been developed for PPRL. They are aimed at reducing the number of record pairs to be compared in the matching process by removing obvious non-matching pairs without compromising privacy. However, they vary widely in their ability to balance competing goals of accuracy, efficiency and security. In this paper, we propose a novel private blocking approach for PPRL based on dynamic k-anonymous blocking and Paillier cryptosystem. In dynamic k-anonymous blocking, our approach dynamically generates blocks satisfying k-anonymity and more accurate values to represent the blocks with varying k. We also propose a novel similarity measure method which performs on the numerical attributes and combines with Paillier cryptosystem to measure the similarity of two blocks in security, which provides strong privacy guarantees that none information reveals. Experiments conducted on a public dataset of voter registration records validate that our approach is scalable to large databases and keeps a high quality of blocking. We compare our method with other techniques and demonstrate the increases in security and accuracy.