Deep neural networks require large amounts of resources which makes them hard
to use on resource constrained devices such as Internet-of-things devices.
Offloading the computations to the cloud can circumvent these constraints but
introduces a privacy risk since the operator of the cloud is not necessarily
trustworthy. We propose a technique that obfuscates the data before sending it
to the remote computation node. The obfuscated data is unintelligible for a
human eavesdropper but can still be classified with a high accuracy by a neural
network trained on unobfuscated images.Comment: ICML 2018 Privacy in Machine Learning and Artificial Intelligence
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