Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference

Edward Chou
Edward Chou
Josh Beal
Josh Beal
Daniel Levy
Daniel Levy

arXiv: Cryptography and Security, Volume abs/1811.09953, 2018.

Cited by: 27|Bibtex|Views131
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

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...More

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