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HE-PEx: Efficient Machine Learning under Homomorphic Encryption using Pruning, Permutation and Expansion. (arXiv:2207.03384v1 [cs.CR])
cs.LG updates on arXiv.org arxiv.org
Privacy-preserving neural network (NN) inference solutions have recently
gained significant traction with several solutions that provide different
latency-bandwidth trade-offs. Of these, many rely on homomorphic encryption
(HE), a method of performing computations over encrypted data. However, HE
operations even with state-of-the-art schemes are still considerably slow
compared to their plaintext counterparts. Pruning the parameters of a NN model
is a well-known approach to improving inference latency. However, pruning
methods that are useful in the plaintext context may lend nearly negligible …
arxiv encryption homomorphic encryption learning machine machine learning pruning