Homomorphic Encryption for Data Science (HE4DS) by Allon Adir (.PDF)+
File Size: 34.0 MB
Homomorphic Encryption for Data Science (HE4DS) by Allon Adir, Ehud Aharoni, Nir Drucker, Ronen Levy
Requirements: .ePUB, .PDF reader, 34.0 MB
Overview: 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. Homomorphic encryption (HE) is a cryptographic primitive that provides unique security guarantees in the privacy enhancing technologies (PETs) ecosystem. HE is a special type of encryption that allows computations to be performed on encrypted data. For example, it enables additions, multiplications, or both on ciphertexts, where the resulting ciphertext can be decrypted to have the same value as if the mathematical operations were performed directly on the encrypted data. This property is called homomorphism, hence the name of the HE primitive. 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.
Genre: Non-Fiction > Tech & Devices
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