Full 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 adversarial aspects such as attacks and provable defenses, to advanced data matching and analytics technologies such as linking complex and/or unstructured data and machine learning-based linkage techniques. Personal Identifiable Information (PII) about individuals, such as customers, taxpayers, patients, and mobile application users, is increasingly collected and linked across disparate data sources to enable 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.
A large body of work has been conducted in the topic of PPRL over the past three decades. This book 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. It provides 360 degrees of the 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.
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.
