Protecting Privacy through Homomorphic Encryption

✍ Scribed by Kristin Lauter, Wei Dai, Kim Laine

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 183
• Category: Library

✦ Synopsis

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 Readers who have preconceived biases based on out-of-date knowledge will see the recent progress made by industrial and academic pioneers on optimizing and standardizing this technology.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.

✦ Table of Contents

Preface
References
Contents
Part I Introduction to Homomorphic Encryption
Introduction to Homomorphic Encryption and Schemes
1 Introduction to Homomorphic Encryption
1.1 Plaintexts and Operations
1.2 Vectors and Special-Purpose Plaintext Data Types
1.3 Ciphertexts
1.4 Symmetric vs. Public-Key Homomorphic Encryption
1.5 Parameters and Security
2 The BGV and BFV Encryption Schemes
2.1 Homomorphic Operations
Two-Argument Operations
Unary Operations
2.2 Parameter Selection
2.3 A BGV/BFV Hello World Example
2.4 Further Information
Maintenance Operations
Evaluation Keys
Data Encoding
Data Movement Operations
References for the BFV Encryption Scheme
References for the BGV Encryption Scheme
3 The CKKS Encryption Scheme
3.1 Homomorphic Operations
Two-Argument Operations
Unary Operations
3.2 Parameter Selection
3.3 A CKKS Hello World Example
3.4 Further Information
Data Encoding
Maintenance Operations
Evaluation Keys
References for the CKKS Scheme
Reference Implementations
4 The DM (FHEW) and CGGI (TFHE) Schemes
4.1 Basic Concepts
4.2 Homomorphic Operations
Simple Mode Plaintext Space and Operations
A DM/CGGI Hello World Example (Using Simple Mode)
Advanced Mode Plaintext Space and Operations
Advanced-Mode CGGI Hello World Example (Corresponds to the DFA in Fig. 1)
4.3 Further Information
Advanced Notes on Parameters
Some More Advanced Operations Are Supported
Maintenance Operations (and More)
Advanced Functionality in the CGGI Encryption Scheme
Difference Between DM and CGGI
Variants of DM/CGGI
Scheme Switching Using CGGI
Reference Implementations
References
Part II Homomorphic Encryption Security Standard
Homomorphic Encryption Standard
1 Homomorphic Encryption Standard Section 1: Recommended Encryption Schemes
1.1 Notation and Definitions
1.2 Properties
1.3 The BGV and B/FV Homomorphic Encryption Schemes
1.4 The GSW Scheme and Bootstrapping
1.5 Other Schemes
1.6 Additional Features & Discussion
2 Homomorphic Encryption Standard Section 2: Recommended Security Parameters
2.1 Hard Problems
2.2 Attacks on LWE and Their Complexity
2.3 The Arora-Ge Attack
2.4 Algebraic Attacks on Instances of Ring-LWE
2.5 Secure Parameter Selection for Ring LWE
Organizers
Contributors
References
Software References for 7 Homomorphic Encryption Libraries
Part III Applications of Homomorphic Encryption
Privacy-Preserving Data Sharing and Computation Across Multiple Data Providers with Homomorphic Encryption
1 Motivation
2 System Models and Use Cases
3 Stakeholders and Functionalities
4 Functionality Goals
5 Threat Models and Security Requirements
6 High-Level Workflow
7 Example Protocol Instantiations
7.1 Distributed Data Discovery (MedCo)
Setup
Initialization
ETL Process
Query Generation
Query Re-encryption
Local Query Processing
Result Obfuscation
Result Shuffling
Proxy Re-encryption of the Result
Decryption
7.2 Centralized Data Analysis (Private Evaluation of Random Forests)
7.3 Distributed Data Analysis (Statistical Computation and Training of Machine Learning Models)
8 Concluding Remarks
References
Secure and Confidential Rule Matching for Network Traffic Analysis
1 Introduction
1.1 Motivation and Business Problem
2 Threat Model
3 Protocol
3.1 Client
3.2 Solution Provider
3.3 Rule Sets
Examples of Rules
3.4 Prerequisites of the Protocol
3.5 Protocol Steps
4 Performance, Usability, and Scalability
4.1 Security Agencies
4.2 Fraud Detection
References
Trusted Monitoring Service (TMS)
1 Privacy-Preserving Health Monitoring
2 Business Motivation
3 Protocol (Workflow)
4 Performance, Usability, Scalability
5 Applications of Trusted Monitoring Systems
References
Private Set Intersection and Compute
1 Motivation
1.1 Privacy Compliance
1.2 Co-marketing as a Use Case
2 Application Functionality
2.1 Database Statistics on PSI Selected Entries
3 Protocol
3.1 Workflow
3.2 First Protocol: N Parties with One Central Compute Node
4 Examples
4.1 IXUP
4.2 Private Join and Compute
5 Performance, Usability, and Scalability
References
Part IV Applications of Homomorphic Encryption
Private Outsourced Translation for Medical Data
1 Introduction
2 Machine Translation
3 Design
3.1 Challenges
4 Implementation and Evaluation
4.1 Encoding
4.2 Optimizations
4.3 Results
5 Discussion
References
HappyKidz: Privacy Preserving Phone Usage Tracking
1 Introduction
1.1 Privacy Model
2 Proof of Concept Implementation
2.1 Data Selection and Features
2.2 Learning Model
2.3 Microsoft SEAL Implementation
3 Soundness and Future Work
3.1 Future Work
4 Conclusion
References
i-SEAL2: Identifying Spam EmAiL with SEAL
1 Introduction
2 Private Classification
3 Private Training
4 Conclusion
PRIORIS: Enabling Secure Detection of Suicidal Ideation from Speech Using Homomorphic Encryption
1 Introduction
2 Suicide Ideation Detection
2.1 Dataset
2.2 Application
3 Use Cases
3.1 Use-Case 1: Secure Detection and Response
3.2 Use-Case 2: Secure Clinical Assessment Assistance
3.3 Use-Case 3: Secure Treatment Evaluation
4 Network Training
5 Homomorphic Network Evaluation
6 Extensions and Future Work
7 Conclusion
References
Gimme That Model!: A Trusted ML Model Trading Protocol
1 Introduction
2 Non-cryptographic Approaches and Their Drawbacks
3 Our HE-Based Cryptographic Solution
3.1 The Protocol
3.2 Efficiency of the Protocol
3.3 Towards the Perfect Model Protection
3.4 Compatible ML Models
4 Discussions
4.1 Plausibility of Trading ML Models
4.2 Alternative Cryptographic Solutions
4.3 Dual Scenario: Trading Datasets
References
HEalth: Privately Computing on Shared Healthcare Data
1 Introduction and Motivation
2 Our Scenario
3 A Discussion of the Underlying Cryptography
4 The Initial Goal: Fairness
5 Discussion
References
Private Movie Recommendations for Children
1 Introduction
1.1 Background
2 Proposed Implementation
2.1 HE Technical Details
3 Discussion
References
Privacy-Preserving Prescription Drug Management Using Fully Homomorphic Encryption
1 Introduction
2 Our Model
3 Fully Homomorphic Encryption
3.1 Our Choice of FHE Scheme
3.2 Updating the Encrypted Records
3.3 Parameters
4 The Machine Learning Model
4.1 Training the Model
4.2 A Remark on Using ML
5 Authentication
5.1 The Shared Secret Key
5.2 Prevent Patient Tampering
References