LEADER 04071nam 22007095 450 001 9910906200703321 005 20241110115722.0 010 $a9783031654947 010 $a3031654943 024 7 $a10.1007/978-3-031-65494-7 035 $a(MiAaPQ)EBC31759760 035 $a(Au-PeEL)EBL31759760 035 $a(CKB)36527731600041 035 $a(DE-He213)978-3-031-65494-7 035 $a(EXLCZ)9936527731600041 100 $a20241110d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHomomorphic Encryption for Data Science (HE4DS) /$fby Allon Adir, Ehud Aharoni, Nir Drucker, Ronen Levy, Hayim Shaul, Omri Soceanu 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (311 pages) 311 08$a9783031654930 311 08$a3031654935 327 $aPart I Introduction and Basic Homomorphic Encryption (HE) Concepts -- Chapter 1 Introduction to Data Science -- Chapter 2 Modern Homomorphic Encryption - Introduction -- Chapter 3 Modern HE - Security Models -- Chapter 4 Approaches for Writing HE Applications -- Part II Approximations -- Chapter 5 Approximation Methods Part I: A General Overview -- Chapter 6 Approximation Methods Part II: Approximations of Standard Functions -- Part III Packing Methods -- Chapter 7 SIMD Packing Part I: Basic Packing Techniques -- Chapter 8 SIMD Packing Part II ? Tile Tensor Basics -- Chapter 9 SIMD Packing Part III: Advanced Tile Tensors -- Part IV Use Cases and Other Approaches -- Chapter 10 Privacy-Preserving Machine Learning with HE -- Chapter 11 Case Study: Neural Network. 330 $aThis book provides basic knowledge required by an application developer to understand and use the Fully Homomorphic Encryption (FHE) technology for privacy preserving Data-Science applications. The authors present various techniques to leverage the unique features of FHE and to overcome its characteristic limitations. Specifically, this book summarizes polynomial approximation techniques used by FHE applications and various data packing schemes based on a data structure called tile tensors, and demonstrates how to use the studied techniques in several specific privacy preserving applications. Examples and exercises are also included throughout this book. The proliferation of practical FHE technology has triggered a wide interest in the field and a common wish to experience and understand it. This book aims to simplify the FHE world for those who are interested in privacy preserving data science tasks, and for an audience that does not necessarily have a deep cryptographic background, including undergraduate and graduate-level students in computer science, and data scientists who plan to work on private data and models. 606 $aData protection$xLaw and legislation 606 $aCryptography 606 $aData encryption (Computer science) 606 $aMachine learning 606 $aComputer networks$xSecurity measures 606 $aPrivacy 606 $aCryptology 606 $aMachine Learning 606 $aMobile and Network Security 615 0$aData protection$xLaw and legislation. 615 0$aCryptography. 615 0$aData encryption (Computer science). 615 0$aMachine learning. 615 0$aComputer networks$xSecurity measures. 615 14$aPrivacy. 615 24$aCryptology. 615 24$aMachine Learning. 615 24$aMobile and Network Security. 676 $a005.8 676 $a323.448 700 $aAdir$b Allon$01775549 701 $aAharoni$b Ehud$01775550 701 $aDrucker$b Nir$01775551 701 $aLevy$b Ronen$01775552 701 $aShaul$b Hayim$01775553 701 $aSoceanu$b Omri$01775554 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910906200703321 996 $aHomomorphic Encryption for Data Science (HE4DS)$94290194 997 $aUNINA