04239nam a22005775i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001500172050001600187072001600203072002300219072001500242100008100257245015900338250001800497264006900515300006500584336002600649337002600675338003600701347002400737505077700761520114101538650004902679650002502728650004702753650002902800650004902829650002002878650002302898650002902921650004002950700008302990700008203073700008103155700008203236700008303318710003403401773002603435776003603461776003603497776003603533856004603569912001403615912001503629999001703644978-3-031-65494-7DE-He21320251030113305.0cr nn 008mamaa241110s2024    sz |    s    |||| 0|eng d  a97830316549479978-3-031-65494-77 a10.1007/978-3-031-65494-72doi 4aQA76.9.A25 4aJC596-596.2 7aURD2bicssc 7aCOM0600402bisacsh 7aURD2thema1 aAdir, Allon.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut98727810aHomomorphic Encryption for Data Science (HE4DS)h[electronic resource] /cby Allon Adir, Ehud Aharoni, Nir Drucker, Ronen Levy, Hayim Shaul, Omri Soceanu.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXXI, 304 p. 93 illus., 90 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 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.  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. 0aData protectionxLaw and legislation.966490 0aCryptography.964299 0aData encryption (Computer science).958243 0aMachine learning.955059 0aComputer networksxSecurity measures.96394914aPrivacy.96649324aCryptology.96649124aMachine Learning.95505924aMobile and Network Security.9663791 aAharoni, Ehud.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9872791 aDrucker, Nir.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9872801 aLevy, Ronen.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9872811 aShaul, Hayim.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9872821 aSoceanu, Omri.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9872832 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303165493008iPrinted edition:z978303165495408iPrinted edition:z978303165496140uhttps://doi.org/10.1007/978-3-031-65494-7  aZDB-2-CWD  aZDB-2-SXPC  c64839d64839