Incremental clustering techniques for multi-party Privacy-Preserving Record Linkage
作者：
Highlights：
• Privacy-preserving linkage of multiple databases is required in several applications.
• Existing work do not support efficient multi-database linkage and subset linkage.
• This paper addresses the problem using graph-based incremental clustering techniques.
• Probabilistic encoding techniques (Bloom filters and counting Bloom filters) are used.
• Experimental results show higher accuracy and efficiency compared to existing methods.
摘要
•Privacy-preserving linkage of multiple databases is required in several applications.•Existing work do not support efficient multi-database linkage and subset linkage.•This paper addresses the problem using graph-based incremental clustering techniques.•Probabilistic encoding techniques (Bloom filters and counting Bloom filters) are used.•Experimental results show higher accuracy and efficiency compared to existing methods.
论文关键词：Data linkage,Privacy,Scalability,Graph matching,Multiple databases,Subset matching
论文评审过程：Received 18 June 2019, Revised 4 November 2019, Accepted 7 March 2020, Available online 16 March 2020, Version of Record 5 August 2020.
论文官网地址：https://doi.org/10.1016/j.datak.2020.101809
