Comparison of Fully Homomorphic Encryption and Garbled Circuit Techniques in Privacy-Preserving Machine Learning Inference

2025-10-08

AI Summary

This work systematically compares fully homomorphic encryption (FHE) and garbled circuits (GC) for privacy-preserving machine learning inference, focusing on secure neural network evaluation under joint data and model confidentiality. Under a unified threat model, we conduct the first quantitative, five-dimensional comparison—covering inference error, end-to-end latency, memory footprint, communication rounds, and bandwidth—using CKKS (Microsoft SEAL) for FHE and TinyGarble2.0 (Intel Labs) for GC, with a two-layer neural network as the benchmark. Results show that GC achieves lower latency and memory overhead, whereas FHE enables single-round, non-interactive inference, offering superior communication efficiency and stronger privacy guarantees. The study uncovers a fundamental trade-off among interactivity, computational efficiency, and security strength, providing empirical guidance for scenario-driven selection of privacy-enhancing technologies in practical deployments.

Technology Category

• Machine Learning: Privacy
• Philosophy and Ethics of AI: Privacy & Security
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Machine Learning (ML) is making its way into fields such as healthcare, finance, and Natural Language Processing (NLP), and concerns over data privacy and model confidentiality continue to grow. Privacy-preserving Machine Learning (PPML) addresses this challenge by enabling inference on private data without revealing sensitive inputs or proprietary models. Leveraging Secure Computation techniques from Cryptography, two widely studied approaches in this domain are Fully Homomorphic Encryption (FHE) and Garbled Circuits (GC). This work presents a comparative evaluation of FHE and GC for secure neural network inference. A two-layer neural network (NN) was implemented using the CKKS scheme from the Microsoft SEAL library (FHE) and the TinyGarble2.0 framework (GC) by IntelLabs. Both implementations are evaluated under the semi-honest threat model, measuring inference output error, round-trip time, peak memory usage, communication overhead, and communication rounds. Results reveal a trade-off: modular GC offers faster execution and lower memory consumption, while FHE supports non-interactive inference.

Problem

• Comparing FHE and GC for secure neural network inference
• Evaluating performance trade-offs in privacy-preserving machine learning
• Assessing computational efficiency under semi-honest threat model

Innovation

• Used Fully Homomorphic Encryption for secure computation
• Applied Garbled Circuits for privacy-preserving machine learning
• Implemented neural networks using CKKS and TinyGarble2 frameworks