全同态加密与混淆电路技术在隐私保护机器学习推理中的比较
Comparison of Fully Homomorphic Encryption and Garbled Circuit Techniques in Privacy-Preserving Machine Learning Inference
- IEEE
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文比较了全同态加密（FHE）与混淆电路（GC）在隐私保护机器学习推理中的性能，发现GC执行更快、内存更低，而FHE支持非交互式推理。
AI中文摘要：
机器学习（ML）正进入医疗、金融和自然语言处理（NLP）等领域，对数据隐私和模型机密性的担忧持续增长。隐私保护机器学习（PPML）通过在不泄露敏感输入或专有模型的情况下对私有数据进行推理来应对这一挑战。利用密码学中的安全计算技术，该领域中两种被广泛研究的方法是全同态加密（FHE）和混淆电路（GC）。本工作对FHE和GC在安全神经网络推理中的性能进行了比较评估。使用微软SEAL库中的CKKS方案（FHE）和IntelLabs的TinyGarble2.0框架（GC）实现了一个两层神经网络（NN）。两种实现在半诚实威胁模型下进行评估，测量推理输出误差、往返时间、峰值内存使用、通信开销和通信轮数。结果揭示了一种权衡：模块化GC提供更快的执行速度和更低的内存消耗，而FHE支持非交互式推理。
英文摘要：
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.
