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1. |
Record Nr. |
UNINA9910792403303321 |
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Autore |
Nāgārjuna <active 2nd century.> |
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Titolo |
The fundamental wisdom of the middle way : Nagarjuna's Mulamadhyamakakarika / / translation and commentary by Jay L. Garfield |
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Pubbl/distr/stampa |
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New York, New York ; ; Oxford, [England] : , : Oxford University Press, , 1995 |
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©1995 |
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ISBN |
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0-19-773923-7 |
0-19-997859-X |
0-19-976632-0 |
1-282-61333-2 |
9786612613333 |
0-19-509336-4 |
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Descrizione fisica |
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1 online resource (393 p.) |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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pt. 1. The text of Mūlamadhyamakakārikā -- pt. 2. The text and commentary. |
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Sommario/riassunto |
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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 |
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2. |
Record Nr. |
UNINA9910906200703321 |
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Autore |
Adir Allon |
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Titolo |
Homomorphic Encryption for Data Science (HE4DS) / / by Allon Adir, Ehud Aharoni, Nir Drucker, Ronen Levy, Hayim Shaul, Omri Soceanu |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (311 pages) |
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Altri autori (Persone) |
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AharoniEhud |
DruckerNir |
LevyRonen |
ShaulHayim |
SoceanuOmri |
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Disciplina |
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Soggetti |
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Data protection - Law and legislation |
Cryptography |
Data encryption (Computer science) |
Machine learning |
Computer networks - Security measures |
Privacy |
Cryptology |
Machine Learning |
Mobile and Network Security |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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 |
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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. |
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Sommario/riassunto |
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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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