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This book provides modern technical answers to the legal requirements of pseudonymisation as recommended by privacy legislation. It covers topics such as modern regulatory frameworks for sharing and linking sensitive information, concepts and algorithms for privacy-preserving record linkage and their computational aspects, practical considerations such as dealing with dirty and missing data, as well as privacy, risk, and performance assessment measures. Existing techniques for privacy-preserving record linkage are evaluated empirically and real-world application examples that scale to population sizes are described. The book also includes pointers to freely available software tools, benchmark data sets, and tools to generate synthetic data that can be used to test and evaluate linkage techniques.
Peter Christen is Professor at the Australian National University (ANU) Research School of Computer Science (RSCS). His research interests are in record linkage and data mining, with a focus on privacy and machine learning aspects of record linkage. He has published nearly 200 articles in these areas, including the monograph "Data Matching" published by Springer in 2012.
Thilina Ranbaduge is a research fellow at the Australian National University (ANU) Research School of Computer Science (RSCS). His research interests are in privacy-preserving record linkage, multi-database linkage, and data mining. He has published more than 30 papers related to record linkage and privacy-preserving record linkage.
Rainer Schnell is Professor at the University of Duisburg-Essen and holds the Chair in Research Methodology in the Social Sciences. He had been Director of the Centre of Comparative Social Surveys at the University of London, City from 2015 to 2017. He is a survey methodologist with a research focus on non-sampling errors, applied sampling, census operations, and privacy-preserving record linkage. He has published four books on research methodology and more than 60 papers on record linkage.
Part I: Introduction.- 1. Introduction.- 2. Regulatory Frameworks.- 3. Linking Sensitive Data Background.- Part II: Methods and Techniques.- 4. Private Information Sharing Protocols.- 5. Assessing Privacy and Risks.- 6. Building Blocks for Linking Sensitive Data.- 7 Encoding and Comparing Sensitive Values.- 8. Bloom Filter based Encoding Methods.- 9. Attacking and Hardening Bloom Filter Encoding.- 10. Computational Efficiency.- Part III: Practical Aspects, Evaluation, and Applications.- 11. Practical Considerations.- 12. Empirical Evaluation.- 13. Real-world Applications.- Part IV: Outlook.- 14. Future Research Challenges and Directions.
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