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  3. Machine learning

Homomorphic Encryption for Data Science (HE4DS)

Af: Omri Soceanu, Ehud Aharoni, Allon Adir, Nir Drucker, Hayim Shaul, Ronen Levy Engelsk Hardback

Homomorphic Encryption for Data Science (HE4DS)

Af: Omri Soceanu, Ehud Aharoni, Allon Adir, Nir Drucker, Hayim Shaul, Ronen Levy Engelsk Hardback
Tjek vores konkurrenters priser

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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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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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 304
ISBN-13: 9783031654930
Indbinding: Hardback
Udgave:
ISBN-10: 3031654935
Kategori: Machine learning
Udg. Dato: 10 nov 2024
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer International Publishing AG
Oplagsdato: 10 nov 2024
Forfatter(e): Omri Soceanu, Ehud Aharoni, Allon Adir, Nir Drucker, Hayim Shaul, Ronen Levy
Forfatter(e) Omri Soceanu, Ehud Aharoni, Allon Adir, Nir Drucker, Hayim Shaul, Ronen Levy

Kategori Machine learning

ISBN-13 9783031654930

Sprog Engelsk

Indbinding Hardback

Sider 304

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 10 nov 2024

Oplagsdato 10 nov 2024

Forlag Springer International Publishing AG

Kategori sammenhænge