Fairness and Cost Constrained Privacy-Aware Record Linkage
Nan Wu, Dinusha Vatsalan, Sunny Verma, and Mohamed Ali Kaafar

TL;DR
This paper introduces new fairness and cost constraints into privacy-preserving record linkage, proposing theoretical frameworks and demonstrating improved performance over standard differential privacy methods.
Contribution
It develops novel fairness and cost-constrained differential privacy notions for PPRL and provides a framework with theoretical proofs and experimental validation.
Findings
Enhanced privacy, fairness, and cost trade-offs achieved
Theoretical proofs support the new DP notions
Experimental results show improved performance over standard DP
Abstract
Record linkage algorithms match and link records from different databases that refer to the same real-world entity based on direct and/or quasi-identifiers, such as name, address, age, and gender, available in the records. Since these identifiers generally contain personal identifiable information (PII) about the entities, record linkage algorithms need to be developed with privacy constraints. Known as privacy-preserving record linkage (PPRL), many research studies have been conducted to perform the linkage on encoded and/or encrypted identifiers. Differential privacy (DP) combined with computationally efficient encoding methods, e.g. Bloom filter encoding, has been used to develop PPRL with provable privacy guarantees. The standard DP notion does not however address other constraints, among which the most important ones are fairness-bias and cost of linkage in terms of number of…
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Taxonomy
TopicsData Quality and Management · Privacy-Preserving Technologies in Data
