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Homomorphic encryption enables arbitrary computation over data while itremains encrypted. This privacy-preserving feature is attractive for machinelearning, but requires significant computational time due to the large overheadof the encryption scheme. We present Faster CryptoNets, a method for efficientencrypted inference using neural networks. We develop a pruning andquantization approach that leverages sparse representations in the underlyingcryptosystem to accelerate inference. We derive an optimal approximation forpopular activation functions that achieves maximally-sparse encodings andminimizes approximation error. We also show how privacy-safe trainingtechniques can be used to reduce the overhead of encrypted inference forreal-world datasets by leveraging transfer learning and differential privacy.Our experiments show that our method maintains competitive accuracy andachieves a significant speedup over previous methods. This work increases theviability of deep learning systems that use homomorphic encryption to protectuser privacy.

Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference