A Multi-Party Privacy-Preserving Record Linkage Method Based on Secondary Encoding

Abstract

With the advent of the big data era, data security and sharing have become the core elements of new-era data processing. Privacy-preserving record linkage (PPRL), as a method capable of accurately and securely matching and sharing the same entity across multiple data sources, is receiving increasing attention. Among the existing research methods, although PPRL methods based on Bloom Filter encoding excel in computational efficiency, they are susceptible to privacy attacks, and the security risks they face cannot be ignored. To balance the contradiction between security and computational efficiency, we propose a multi-party PPRL method based on secondary encoding. This method, based on Bloom Filter encoding, generates secondary encoding according to well-designed encoding rules and utilizes the proposed linking rules for secure matching. Owing to its excellent encoding and linking rules, this method successfully addresses the balance between security and computational efficiency. The experimental results clearly show that, in comparison to the original Bloom Filter encoding, this method has nearly equivalent computational efficiency and linkage quality. The proposed rules can effectively prevent the re-identification problem in Bloom Filter encoding (proven). Compared to existing privacy-preserving record linkage methods, this method shows higher security, making it more suitable for various practical application scenarios. The introduction of this method is of great significance for promoting the widespread application of privacy-preserving record linkage technology.