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.
Leroux et al. REF use an autoencoder to obfuscate the data before sending it to the cloud, but the obfuscation they use is readily reversible, as they state.
Privacy Aware Offloading of Deep Neural Networks
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