Multi-Layer Privacy-Preserving Record Linkage with Clerical Review based on gradual information disclosure
Privacy-Preserving Record linkage (PPRL) is an essential component in data integration tasks of sensitive information. The linkage quality determines the usability of combined datasets and (machine learning) applications based on them. We present a novel privacy-preserving protocol that integrates clerical review in PPRL using a multi-layer active learning process. Uncertain match candidates are reviewed on several layers by human and non-human oracles to reduce the amount of disclosed information per record and in total. Predictions are propagated back to update previous layers, resulting in an improved linkage performance for non-reviewed candidates as well. The data owners remain in control of the amount of information they share for each record. Therefore, our approach follows need-to-know and data sovereignty principles. The experimental evaluation on real-world datasets shows considerable linkage quality improvements with limited labeling effort and privacy risks.
Code (0)
등록된 구현이 없습니다.
Tasks
Active LearningData IntegrationPrivacy PreservingSimilar Papers 제목 키워드 기반
Privacy-preserving Deep Learning based Record Linkage
Deep learning-based linkage of records across different databases is becoming increasingly useful in data integration and mining applications to discover new insights from multiple sources of data. However, due to privac…
Data IntegrationDeep LearningPrivacy PreservingPrivacy Preserving Deep LearningTowards Split Learning-based Privacy-Preserving Record Linkage
Split Learning has been recently introduced to facilitate applications where user data privacy is a requirement. However, it has not been thoroughly studied in the context of Privacy-Preserving Record Linkage, a problem …
Privacy PreservingPrivacy-Preserving Record Linkage for Cardinality Counting
Several applications require counting the number of distinct items in the data, which is known as the cardinality counting problem. Example applications include health applications such as rare disease patients counting …
ClusteringMarketingPrivacy PreservingPrivacy-Preserving EHR Data Transformation via Geometric Operators: A Human-AI Co-Design Technical Report
Electronic health records (EHRs) and other real-world clinical data are essential for clinical research, medical artificial intelligence, and life science, but their sharing is severely limited by privacy, governance, an…
REAEDP: Entropy-Calibrated Differentially Private Data Release with Formal Guarantees and Attack-Based Evaluation
Sensitive data release is vulnerable to output-side privacy threats such as membership inference, attribute inference, and record linkage. This creates a practical need for release mechanisms that provide formal privacy …