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Shredder: Learning Noise Distributions to Protect Inference Privacy
[article]
2020
arXiv
pre-print
A wide variety of deep neural applications increasingly rely on the cloud to perform their compute-heavy inference. This common practice requires sending private and privileged data over the network to remote servers, exposing it to the service provider and potentially compromising its privacy. Even if the provider is trusted, the data can still be vulnerable over communication channels or via side-channel attacks in the cloud. To that end, this paper aims to reduce the information content of
arXiv:1905.11814v3
fatcat:inv2dc4aavbmdmio6gv7ysoirq