Towards Auditable and Intelligent Privacy-Preserving Record Linkage

Abstract

Privacy-Preserving Record Linkage (PPRL) intends to integrate private/sensitive data from several data sources held by different parties. It aims to identify records (e.g., persons or objects) representing the same real-world entity over private data sources held by different custodians. Due to recent laws and regulations (e.g., General Data Protection Regulation), PPRL approaches are increasingly demanded in real-world application areas such as health care, credit analysis, public policy evaluation, and national security. As a result, the PPRL process needs to deal with efficacy (linkage quality), and privacy problems. For instance, the PPRL process needs to be executed over data sources (e.g., a database containing personal information of governmental income distribution and assistance programs), with an accurate linkage of the entities, and, at the same time, protect the privacy of the information. Thus, this work intends to simplify the PPRL process by facilitating real-world applications (such as medical, epidemiologic, and populational studies) to reduce legal and bureaucratic efforts to access and process the data, making these applications' execution more straightforward for companies and governments. In this context, this work presents two major contributions to PPRL: i) an improvement to the linkage quality and simplify the process by employing Machine Learning techniques to decide whether two records represent the same entity, or not; and ii) we enable the auditability the computations performed during PPRL.