Protecting Privacy through Homomorphic Encryption
- Length: 192 pages
- Edition: 1
- Language: English
- Publisher: Springer
- Publication Date: 2022-02-04
- ISBN-10: 3030772861
- ISBN-13: 9783030772864
- Sales Rank: #3523934 (See Top 100 Books)
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 Readers who have preconceived biases based on out-of-date knowledge will see the recent progress made by industrial and academic pioneers on optimizing and standardizing this technology.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.
Cover Front Matter Part I. Introduction to Homomorphic Encryption Introduction to Homomorphic Encryption and Schemes Part II. Homomorphic Encryption Security Standard Homomorphic Encryption Standard Part III. Applications of Homomorphic Encryption Privacy-Preserving Data Sharing and Computation Across Multiple Data Providers with Homomorphic Encryption Secure and Confidential Rule Matching for Network Traffic Analysis Trusted Monitoring Service (TMS) Private Set Intersection and Compute Part IV. Applications of Homomorphic Encryption Private Outsourced Translation for Medical Data HappyKidz: Privacy Preserving Phone Usage Tracking i-SEAL2: Identifying Spam EmAiL with SEAL PRIORIS: Enabling Secure Detection of Suicidal Ideation from Speech Using Homomorphic Encryption Gimme That Model!: A Trusted ML Model Trading Protocol HEalth: Privately Computing on Shared Healthcare Data Private Movie Recommendations for Children Privacy-Preserving Prescription Drug Management Using Fully Homomorphic Encryption
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