Making Computation On Encrypted Data Practical Through Hardware Acceleration Of Fully Homomorphic Encryption
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Making Computation on Encrypted Data Practical Through Hardware Acceleration of Fully Homomorphic Encryption
Author: Nikola Samardzic (Researcher in electrical engineering and computer science)
language: en
Publisher:
Release Date: 2022
Fully Homomorphic Encryption (FHE) enables offloading computation to untrusted servers with cryptographic privacy. Despite its attractive security, FHE is not yet widely adopted due to its prohibitive overheads, about 10,000x over unencrypted computation.
Advances to Homomorphic and Searchable Encryption
Author: Stefania Loredana Nita
language: en
Publisher: Springer Nature
Release Date: 2023-09-26
This book presents the current state of the literature on the fields of homomorphic and searchable encryption, from both theoretical and practical points of view. Homomorphic and searchable encryption are still relatively novel and rapidly evolving areas and face practical constraints in the contexts of large-scale cloud computing and big data. Both encryption methods can be quantum-resistant if they use the right mathematical techniques. In fact, many fully homomorphic encryption schemes already use quantum-resistant techniques, such as lattices or characteristics of polynomials – which is what motivated the authors to present them in detail. On the one hand, the book highlights the characteristics of each type of encryption, including methods, security elements, security requirements, and the main types of attacks that can occur. On the other, it includes practical cases and addresses aspects like performance, limitations, etc. As cloud computing and big data already represent the future in terms of storing, managing, analyzing, and processing data, these processes need to be made as secure as possible, and homomorphic and searchable encryption hold huge potential to secure both the data involved and the processes through which it passes. This book is intended for graduates, professionals and researchers alike. Homomorphic and searchable encryption involve advanced mathematical techniques; accordingly, readers should have a basic background in number theory, abstract algebra, lattice theory, and polynomial algebra.
Protecting Privacy through Homomorphic Encryption
This book summarizes recent inventions, provides guidelines and recommendations, and demonstrates many practical applications of homomorphic encryption. This collection of papers represents the combined wisdom of the community of leading experts on Homomorphic Encryption. In the past 3 years, a global community consisting of researchers in academia, industry, and government, has been working closely to standardize homomorphic encryption. This is the first publication of whitepapers created by these experts that comprehensively describes the scientific inventions, presents a concrete security analysis, and broadly discusses applicable use scenarios and markets. This book also features a collection of privacy-preserving machine learning applications powered by homomorphic encryption designed by groups of top graduate students worldwide at the Private AI Bootcamp hosted by Microsoft Research. The volume aims to connect non-expert readers with this important new cryptographic technology in an accessible and actionable way. Readers who have heard good things about homomorphic encryption but are not familiar with the details will find this book full of inspiration. Readers who have preconceived biases based on out-of-date knowledge will see the recent progress made by industrial and academic pioneers on optimizing and standardizing this technology. A clear picture of how homomorphic encryption works, how to use it to solve real-world problems, and how to efficiently strengthen privacy protection, will naturally become clear.