04498nam a22006375i 4500 978-3-031-65494-7 DE-He213 20251030113305.0 cr nn 008mamaa 241110s2024 sz | s |||| 0|eng d 9783031654947 978-3-031-65494-7 10.1007/978-3-031-65494-7 doi QA76.9.A25 JC596-596.2 URD bicssc COM060040 bisacsh URD thema Adir, Allon. author. aut http://id.loc.gov/vocabulary/relators/aut 87278 Homomorphic Encryption for Data Science (HE4DS) [electronic resource] / by Allon Adir, Ehud Aharoni, Nir Drucker, Ronen Levy, Hayim Shaul, Omri Soceanu. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XXI, 304 p. 93 illus., 90 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Part 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. This 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. Data protection Law and legislation. 66490 Cryptography. 64299 Data encryption (Computer science). 58243 Machine learning. 55059 Computer networks Security measures. 63949 Privacy. 66493 Cryptology. 66491 Machine Learning. 55059 Mobile and Network Security. 66379 Aharoni, Ehud. author. aut http://id.loc.gov/vocabulary/relators/aut 87279 Drucker, Nir. author. aut http://id.loc.gov/vocabulary/relators/aut 87280 Levy, Ronen. author. aut http://id.loc.gov/vocabulary/relators/aut 87281 Shaul, Hayim. author. aut http://id.loc.gov/vocabulary/relators/aut 87282 Soceanu, Omri. author. aut http://id.loc.gov/vocabulary/relators/aut 87283 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031654930 Printed edition: 9783031654954 Printed edition: 9783031654961 https://doi.org/10.1007/978-3-031-65494-7 ZDB-2-CWD ZDB-2-SXPC 64839 64839