Springer - Linking Sensitive Data: Privacy-Preserving Methods

Navigate the complexities of data privacy with Linking Sensitive Data: Methods and Techniques for Practical Privacy-Preserving Information Sharing. Published by Springer, this book offers technical solutions to meet the legal requirements of pseudonymisation as defined by modern privacy legislation. Readers will gain a deep understanding of regulatory frameworks for sharing and linking sensitive information. The text explores essential concepts and algorithms for privacy-preserving record linkage, addressing the computational aspects necessary for modern data science. Beyond theory, the book covers practical challenges such as managing dirty and missing data, alongside methods for privacy, risk, and performance assessment. By evaluating existing techniques through empirical studies and real-world application examples that scale to large populations, this resource serves as a practical guide for professionals working in AI, machine learning, and data security. It provides the tools needed to balance data utility with strict privacy standards in real-world environments.