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Autor(en): 
  • Allon Adir
  • Ehud Aharoni
  • Nir Drucker
  • Ronen Levy
  • Hayim Shaul
  • Omri Soceanu
  • Homomorphic Encryption for Data Science (HE4DS) 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  November 2024  
    Genre:  EDV / Informatik 
     
    Applied cryptography / Approximated computations / Cloud-based trust-models / Cryptology / Encrypted models / Homomorphic encryption (HE) / Kodierungstheorie und Verschlüsselung (Kryptologie) / Kryptografie, Verschlüsselungstechnologie
    ISBN:  9783031654930 
    EAN-Code: 
    9783031654930 
    Verlag:  Springer EN 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D 24 mm 
    Gewicht:  656 gr 
    Seiten:  304 
    Illustration:  XXI, 304 p. 93 illus., 90 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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.

      



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