LEADER 03562nam 22006375 450 001 9911015687303321 005 20250716130245.0 010 $a3-031-95140-9 024 7 $a10.1007/978-3-031-95140-4 035 $a(MiAaPQ)EBC32212888 035 $a(Au-PeEL)EBL32212888 035 $a(CKB)39659073100041 035 $a(DE-He213)978-3-031-95140-4 035 $a(EXLCZ)9939659073100041 100 $a20250716d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPrivacy Enhancing Techniques $ePractices and Applications /$fby Xun Yi, Xuechao Yang, Xiaoning Liu, Andrei Kelarev, Kwok-Yan Lam, Mengmeng Yang, Xiangning Wang, Elisa Bertino 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (367 pages) 311 08$a3-031-95139-5 327 $aChapter 1: Introduction -- Chapter 2: Homomorphic Encryption -- Chapter 3: Multiparty Computation -- Chapter 4: Differential Privacy -- Chapter 5: Privacy-Preserving Data Mining -- Chapter 6: Privacy-Preserving Machine Learning -- Chapter 7: Privacy-Preserving Social Networks -- Chapter 8: Privacy-Preserving Location-Based Services -- Chapter 9: Privacy and Digital Trust -- Chapter 10: Conclusion. 330 $aThis 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. 606 $aData protection$xLaw and legislation 606 $aData mining 606 $aMachine learning 606 $aPrivacy 606 $aData Mining and Knowledge Discovery 606 $aMachine Learning 615 0$aData protection$xLaw and legislation. 615 0$aData mining. 615 0$aMachine learning. 615 14$aPrivacy. 615 24$aData Mining and Knowledge Discovery. 615 24$aMachine Learning. 676 $a005.8 676 $a323.448 700 $aYi$b Xun$0968566 701 $aYang$b Xuechao$01833798 701 $aLiu$b Xiaoning$01786166 701 $aKelarev$b Andrei$0150423 701 $aLam$b Kwok-Yan$01771294 701 $aYang$b Mengmeng$01833799 701 $aWang$b Xiangning$01833800 701 $aBertino$b Elisa$0754415 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911015687303321 996 $aPrivacy Enhancing Techniques$94408743 997 $aUNINA