Description
This is the first book on Privacy-Preserving Record Linkage (PPRL) that provides a comprehensive coverage of the different aspects, ranging from ethical considerations such as fairness-bias in record linkage, and advanced data matching and analytics technologies. This would include linking complex and/or unstructured data and machine learning-based privacy-preserving linkage techniques, to record linkage techniques with provable privacy guarantees. It provides 360 degrees of evolving and contemporary topic covering all the different aspects required to the understanding, designing and implementation of sound and practical PPRL solutions for real-world applications.
Personal Identifiable Information (PII) about individuals, such as customers, taxpayers, patients, and mobile application users, is increasingly collected and linked across disparate data sources. It enables customized, high-quality, and timely analytical services in a variety of applications. The data needed for the linkage is, however, often personal, and sensitive, and needs to be processed using privacy-preserving techniques. Known as privacy-preserving record linkage (PPRL), a large body of work has been conducted in this topic over the past three decades. This book also covers the technological, adversarial, ethical, and analytical developments in PPRL to provide a comprehensive view of PPRL for implementing practical applications in the Big Data and Analytics Era.
This book targets advanced-level students focused on data privacy, record linkage, and data analytics as well as researchers working in this related field. Data science or data linkage practitioners in different domains including health, security, games, business, and finance will also find this book a valuable resource.
Chapter 1: The pressing need for ethical and privacy preserving record linkage: Theory, Industrial Applications and Challenges.- PART 1: TECHNOLOGICAL DEVELOPMENTS.- Chapter 2: Data encoding.- Chapter 3: Blocking and computational aspects.- Chapter 4: Probabilistic Linkage.- Chapter 5: Machine learning and deep learning linkage techniques.- Chapter 6: Hyper-parameter optimisation for linkage.- PART 2: ADVERSARIAL DEVELOPMENTS.- Chapter 7: Privacy implications on record linkage.- Chapter 8: Privacy risk quantification and evaluation.- Chapter 9: Defenses.- PART 3: ETHICAL AND ANALYTICAL DEVELOPMENTS.- Chapter 10: Societal challenges for linkage.- Chapter 11: Human-interactive linkage.- Chapter 12: Real-time and dynamic linkage and analytics.- Chapter 13: Limitations, research directions and open questions.- Chapter 14: Conclusion.
Dinusha Vatsalan is a Senior Lecturer in IT (Higher Grade) at the Northern Uni, Sri Lanka, and an Honorary Lecturer at Macquarie University, Australia. She received her PhD in Computer Science from Australian National University and BSc (Hons) in Information and Communication Technology from University of Colombo, Sri Lanka. She was previously a Senior Lecturer in Cyber Security at Macquarie University, Australia and a Research Scientist at the Australian government's Commonwealth Scientific and Industrial Research Organization (CSIRO). Her research interests are in cybersecurity and privacy-preserving techniques, including privacy in data matching and record linkage, privacy in social media, privacy-preserving data analytics in stream data, privacy risk evaluation and prediction, health informatics, and population informatics. She has published over 80 articles in high-ranked venues on these topics, which have attracted more than 2550 citations.
Hassan Jameel Asghar is a Senior Lecturer and researcher at Macquarie University, specializing in privacy, cryptography, and information security. He has a PhD from the Department of Computing, Macquarie University, and has previously worked as a Research Scientist at Data61, CSIRO. Asghar is a member of the Information Security and Privacy Research Group at Macquarie University and has contributed to various research and industry projects related to security and privacy. He has authored over 80 articles on security and privacy related topics including quantitative privacy risk assessment, privacy-preserving access to and release of data, and secure protocols for outsourced computation.
Dali Kaafar is a Professor at Macquarie University and Executive Director of Macquarie University Cyber Security Hub. He obtained a Ph.D. in Computer Science from University of Nice Sophia Antipolis at Inria France. He was the founder of the Information Security and Privacy Group and leader of the Networks group at CSIRO Data61. He was previously a Senior Principal Researcher, Research leader and a principal researcher at the Mobile Networks Systems group at NICTA and a researcher at the Privatics team at INRIA in France. Dali has made significant contributions to the fields of cybersecurity, privacy, and AI. His research interests include digital privacy, distributed systems security, authentication systems, and security risks measurement and modeling. He has authored over 300 scientific peer-reviewed papers and has advised governments on scam prevention policy. He is also the founder and CEO of Apate.ai, a company that builds large-scale conversational bots to disrupt global scams and extract threat intelligence in real time.
