Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference
Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference
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发表时间：
2018-11
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通讯作者：
Edward Chou;Josh Beal;Daniel Levy;Serena Yeung;Albert Haque;Li Fei-Fei-Li-Fei-Fei-48004138
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作者：
Edward Chou;Josh Beal;Daniel Levy;Serena Yeung;Albert Haque;Li Fei-Fei-Li-Fei-Fei-48004138
同态加密可以在数据保持加密状态的同时对数据进行任意计算。这种隐私保护功能对机器学习很有吸引力，但由于加密方案的开销很大，因此需要大量的计算时间。我们提出了 Faster CryptoNets，这是一种使用神经网络进行高效加密推理的方法。我们开发了一种修剪和量化方法，利用底层密码系统中的稀疏表示来加速推理。我们推导了流行激活函数的最佳近似，该近似实现了最大稀疏编码并最小化了近似误差。我们还展示了如何通过利用迁移学习和差分隐私，使用隐私安全训练技术来减少现实世界数据集加密推理的开销。我们的实验表明，我们的方法保持了有竞争力的准确性，并比以前的方法实现了显着的加速。这项工作提高了使用同态加密来保护用户隐私的深度学习系统的可行性。
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.