1.

Record Nr.

UNINA9910792403303321

Autore

Nāgārjuna <active 2nd century.>

Titolo

The fundamental wisdom of the middle way : Nagarjuna's Mulamadhyamakakarika / / translation and commentary by Jay L. Garfield

Pubbl/distr/stampa

New York, New York ; ; Oxford, [England] : , : Oxford University Press, , 1995

©1995

ISBN

0-19-773923-7

0-19-997859-X

0-19-976632-0

1-282-61333-2

9786612613333

0-19-509336-4

Descrizione fisica

1 online resource (393 p.)

Disciplina

294.3/85

Soggetti

Mādhyamika (Buddhism)

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

pt. 1. The text of Mūlamadhyamakakārikā -- pt. 2. The text and commentary.

Sommario/riassunto

For nearly two thousand years Buddhism has mystified and captivated both lay people and scholars alike. Seen alternately as a path to spiritual enlightenment, an system of ethical and moral rubrics, a cultural tradition, or simply a graceful philosophy of life, Buddhism has produced impassioned followers the world over. The Buddhist saint Nagarjuna, who lived in South India in approximately the first century CE, is undoubtedly the most important, influential, and widely studied Mahayana Buddhist philosopher. His many works include texts addressed to lay audiences, letters of advice to kings, a



2.

Record Nr.

UNINA9910906200703321

Autore

Adir Allon

Titolo

Homomorphic Encryption for Data Science (HE4DS) / / by Allon Adir, Ehud Aharoni, Nir Drucker, Ronen Levy, Hayim Shaul, Omri Soceanu

Pubbl/distr/stampa

Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024

ISBN

9783031654947

3031654943

Edizione

[1st ed. 2024.]

Descrizione fisica

1 online resource (311 pages)

Altri autori (Persone)

AharoniEhud

DruckerNir

LevyRonen

ShaulHayim

SoceanuOmri

Disciplina

005.8

323.448

Soggetti

Data protection - Law and legislation

Cryptography

Data encryption (Computer science)

Machine learning

Computer networks - Security measures

Privacy

Cryptology

Machine Learning

Mobile and Network Security

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

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.

Sommario/riassunto

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.