[論文レビュー] Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference

Edward Chou, Josh Beal+4 more|arXiv (Cornell University)|Nov 25, 2018

Privacy-Preserving Technologies in Data参考文献 41被引用数 137

ひとこと要約

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ABSTRACT

Homomorphic encryption enables arbitrary computation over data while it remains encrypted. This privacy-preserving feature is attractive for machine learning, but requires significant computational time due to the large overhead of the encryption scheme. We present Faster CryptoNets, a method for efficient encrypted inference using neural networks. We develop a pruning and quantization approach that leverages sparse representations in the underlying cryptosystem to accelerate inference. We derive an optimal approximation for popular activation functions that achieves maximally-sparse encodings and minimizes approximation error. We also show how privacy-safe training techniques can be used to reduce the overhead of encrypted inference for real-world datasets by leveraging transfer learning and differential privacy. Our experiments show that our method maintains competitive accuracy and achieves a significant speedup over previous methods. This work increases the viability of deep learning systems that use homomorphic encryption to protect user privacy.

研究の動機と目的

• Motivate privacy-preserving ML via homomorphic encryption for encrypted inference in MLaaS scenarios.
• Develop methods to reduce the computational overhead of encrypted neural networks through sparsity and quantized polynomial representations.
• Derive an optimal sparse polynomial activation approximation compatible with HE to minimize error.
• Show practical viability with transfer learning and differential privacy in real-world datasets.

提案手法

• Introduce network pruning and progressive quantization to enforce sparsity in model weights.
• Represent remaining weights as powers of two to enable sparse polynomial encodings in HE.
• Develop an optimal quantized polynomial approximation of activation functions under power-of-two coefficient constraints.
• Use sparse plaintext-ciphertext multiplication to reduce homomorphic operation counts (HOPs) via monomial multipliers.
• Adopt a feature-extraction framework and differential privacy to improve real-world applicability while maintaining accuracy.
• Evaluate using MNIST and compare HOPs, runtimes, and accuracy against prior encrypted inference methods.

実験結果

リサーチクエスチョン

• RQ1 Can sparsity and quantized polynomial representations substantially reduce the HOPs and wall-clock time of encrypted neural network inference?
• RQ2 What is the trade-off between activation function expressivity and approximation error under power-of-two coefficient constraints?
• RQ3 How do pruning, quantization, and activation approximation affect accuracy on real-world datasets under HE constraints?
• RQ4 Is the approach viable for single-image encrypted inference and scalable to practical tasks?

主な発見

• Faster CryptoNets achieves large reductions in homomorphic operations across layers, leading to faster encrypted inference than CryptoNets and CryptoDL variants.
• The proposed activation approximations with coefficients as powers of two maintain competitive test accuracy on MNIST.
• Sparse plaintext-ciphertext multiplication enables O(n) complexity for certain monomial multipliers, reducing per-layer HOPs.
• Quantization and pruning reduce multiplications, lowering overall HOPs while preserving inference accuracy.
• Comparison shows Faster CryptoNets with Swish-AQ achieves substantially lower total HOPs and faster inference times than prior encrypted methods, with competitive accuracy.
• The work demonstrates viability of real-world encrypted inference with differential privacy and feature extraction to handle larger datasets.