Homomorphic Encryption for Privacy Engineers

Preface

Author’s Motivation

Homomorphic Encryption (HE) stands out as a powerful cryptographic tool, offering a range of possibilities by enabling direct computation on encrypted data, thereby keeping it confidential during processing. HE offers to unlock new use cases in secure cloud computing, collaborative analysis, and privacy-preserving machine learning.

However, a significant gap exists between the elegant theory of HE and its practical application in real-world systems. While academic literature delves deep into the cryptographic foundations , engineers on the front lines—privacy engineers, software developers, data scientists, and security architects—often lack a dedicated, practical guide focused on implementing HE to solve concrete privacy engineering challenges.

This book was written to bridge that gap. My motivation is to translate the complex, often intimidating, world of advanced cryptography into actionable knowledge for the builders and protectors of our digital infrastructure. I aim to provide the intuition behind the cryptography, illustrate concepts with examples (conceptual or code-based where appropriate), and highlight the real-world trade-offs engineers face.

Book Structure

• Part I: Foundations of Homomorphic Encryption for Privacy Engineers lays the groundwork. We start with the “why”—the privacy imperative in modern systems—and then build up the necessary cryptographic concepts, from public-key principles to the lattice-based problems like LWE and RLWE that underpin modern HE. We explore the different types of HE (PHE, SHE, FHE) , the critical challenge of noise management , and delve into the specifics of key schemes like BGV, BFV, and CKKS.  
• Part II: Applying HE Standalone in Privacy Engineering moves from theory to practice, focusing on using HE as a direct tool for privacy tasks. We cover secure statistical analysis , encrypted database queries , Private Information Retrieval (PIR) , and Private Set Intersection (PSI). Crucially, we also address the practicalities of implementation, library choices , and performance considerations.  
• Part III: Privacy-Preserving Machine Learning with HE explores the powerful synergy between HE and machine learning (PPML). We introduce core PPML concepts and then detail how to perform inference for models like linear/logistic regression and neural networks on encrypted data. We tackle advanced protocols like Private Nearest Neighbor Search (PNNS) and Private Decision Tree evaluation , and discuss hybrid approaches combining HE with other PETs like MPC and DP.  

Intended Audience

The primary target audience for this book includes:

• Privacy Engineers: Professionals responsible for designing, building, and maintaining privacy-preserving systems and features.
• Software Developers & Engineers: Individuals implementing systems that handle sensitive data and require privacy controls.
• Data Scientists & Machine Learning Engineers: Practitioners working with sensitive datasets who need to build and deploy privacy-preserving models for training and inference.
• Security Architects: Professionals designing secure system architectures that incorporate advanced cryptographic techniques for data protection.
• Applied Cryptographers & Researchers: Individuals seeking a practical understanding of HE applications beyond theoretical foundations.
• Technical Product Managers: Individuals involved in defining requirements and overseeing the development of products incorporating privacy features.

How to Use This Book

• For those new to HE: Start from the beginning. Part I provides the essential conceptual building blocks.
• For cryptographers: You might skim the foundational chapters in Part I but will find the practical application details in Parts II and III particularly valuable.
• For ML practitioners: Part I, Chapter 1 and Part I, Chapter 3 offer crucial context on HE’s role and limitations. Part III is likely your main focus, detailing HE’s application in ML tasks.  
• For privacy engineers/architects: The entire book provides a comprehensive view, from fundamentals to specific applications and implementation challenges.