Privacy Enhancing Techniques
Xun Yi; Xuechao Yang; Xiaoning Liu; Andrei Kelarev; Kwok-Yan Lam; Mengmeng Yang; Xiangning Wang; Elisa Bertino
Springer International Publishing AG
2025
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This book provides a comprehensive exploration of advanced privacy-preserving methods, ensuring secure data processing across various domains. This book also delves into key technologies such as homomorphic encryption, secure multiparty computation, and differential privacy, discussing their theoretical foundations, implementation challenges, and real-world applications in cloud computing, blockchain, artificial intelligence, and healthcare. With the rapid growth of digital technologies, data privacy has become a critical concern for individuals, businesses, and governments. The chapters cover fundamental cryptographic principles and extend into applications in privacy-preserving data mining, secure machine learning, and privacy-aware social networks. By combining state-of-the-art techniques with practical case studies, this book serves as a valuable resource for those navigating the evolving landscape of data privacy and security. Designed to bridge theory and practice, this book is tailored for researchers and graduate students focused on this field. Industry professionals seeking an in-depth understanding of privacy-enhancing technologies will also want to purchase this book.
