These days neural networks and fully homomorphic encryption are a meme. For example, Microsoft demonstrated with Cryptonets of a neural network generating predictions fully homomorphically on the MNIST dataset. However, it would be useful to have a way to preprocess images homomorphically. Consider the use case where an edge device sends a homomorphically encrypted image to a server that runs a prediction algorithm with two neural networks that take in different sized features, as is common. It would be prohibitive to make the edge device homomorphically encrypt two copies of the images, since that would be prohibitively expensive. Therefore, having proprocessing and feature extraction computed homomorphically will provide much more flexibility for homomorphic neural nets.
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