Data Matching and Privacy

Dear Colleagues,

Data matching has long been a research area of paramount importance. Its objective is to compare data originating from distinct datasets belonging to the same or distinct organizations and locate the same entities. Then, these datasets may be integrated so as to allow for data analysis of enriched data corpora, offering the capacity to generate new knowledge in various domains. Nowadays, the ever-increasing production of personal data has posed a new challenge for data matching, which is privacy preservation. An indicator of the importance of privacy preservation is the fact that it has been acknowledged by the legislations of many countries. Considering this limitation, new methodologies need to emerge that are capable of performing data matching while preserving privacy. In this Special Issue, we aim to host the latest developments in the area of data matching with respect to privacy. As such, we invite high-quality contributions on novel algorithms, techniques and systems that are able to maintain privacy for the task of data matching. Topics of interest include:

• Privacy-preserving matching of relational data;
• Privacy-preserving matching of non-relational data;
• Privacy-preserving matching of data streams;
• Federated learning approaches to privacy-preserving data matching;
• Machine learning for privacy-preserving data matching;
• Measuring the quality of privacy-preserving matching;
• Data matching and privacy in the cloud;
• Quantifying the privacy offered by privacy-preserving data matching;
• Bias and fairness in privacy-preserving data matching;
• Novel applications of privacy-preserving data matching;
• Big data matching and privacy;
• Novel similarity measures for approximate privacy-preserving data matching;
• Scalable privacy-preserving data matching;
• Privacy-preserving data matching from heterogeneous sources;
• Privacy-preserving data matching systems.

Dr. Alexandros Karakasidis
Guest Editor

Manuscript Submission Information

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• Privacy preservation
• Data matching
• Data quality