2017/01/05 by Dinusha Vatsalan, Vatsalan, Dinusha, Peter Christen +3
Computer Science · Decision Sciences · #Cloud Data Security Solutions #Data Quality and Management #Databases (cs.DB) #FOS: Computer and information sciences #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.1701.01232
openalex publication_date 2017/01/05 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
Privacy-preserving record linkage (PPRL) aims at integrating sensitive\ninformation from multiple disparate databases of different organizations. PPRL\napproaches are increasingly required in real-world application areas such as\nhealthcare, national security, and business. Previous approaches have mostly\nfocused on linking only two databases as well as the use of a dedicated linkage\nunit. Scaling PPRL to more databases (multi-party PPRL) is an open challenge\nsince privacy threats as well as the computation and communication costs for\nrecord linkage increase significantly with the number of databases. We thus\npropose the use of a new encoding method of sensitive data based on Counting\nBloom Filters (CBF) to improve privacy for multi-party PPRL. We also\ninvestigate optimizations to reduce communication and computation costs for\nCBF-based multi-party PPRL with and without the use of a dedicated linkage\nunit. Empirical evaluations conducted with real datasets show the viability of\nthe proposed approaches and demonstrate their scalability, linkage quality, and\nprivacy protection.\n