Accelerating probabilistic privacy-preserving medical record linkage: A three-party MPC approach.

Abstract

Record linkage is essential for integrating data from multiple sources with diverse applications in real-world healthcare and research. Probabilistic Privacy-Preserving Record Linkage (PPRL) enables this integration occurs, while protecting sensitive information from unauthorized access, especially when datasets lack exact identifiers. As privacy regulations evolve and multi-institutional collaborations expand globally, there is a growing demand for methods that effectively balance security, accuracy, and efficiency. However, ensuring both privacy and scalability in large-scale record linkage remains a key challenge. This paper presents a novel and efficient PPRL method based on a secure 3-party computation (MPC) framework. Our approach allows multiple parties to compute linkage results without exposing their private inputs and significantly improves the speed of linkage process compared to existing PPRL solutions. Our method preserves the linkage quality of a state-of-the-art (SOTA) MPC-based PPRL method while achieving up to 14 times faster performance. For example, linking a record against a database of 10,000 records takes just 8.74 s in a realistic network with 700 Mbps bandwidth and 60 ms latency, compared to 92.32 s with the SOTA method. Even on a slower internet connection with 100 Mbps bandwidth and 60 ms latency, the linkage completes in 28 s, where as the SOTA method requires 287.96 s. These results demonstrate the significant scalability and efficiency improvements of our approach. Our novel PPRL method, based on secure 3-party computation, offers an efficient and scalable solution for large-scale record linkage while ensuring privacy protection. The approach demonstrates significant performance improvements, making it a promising tool for secure data integration in privacy-sensitive sectors.