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Machine learning as a service has been widely deployed to utilize deep neuralnetwork models to provide prediction services. However, this raises privacyconcerns since clients need to send sensitive information to servers. In thispaper, we focus on the scenario where clients want to classify private imageswith a convolutional neural network model hosted in the server, while bothparties keep their data private. We present FALCON, a fast and secure approachfor CNN predictions based on Fourier Transform. Our solution enables linearlayers of a CNN model to be evaluated simply and efficiently with fullyhomomorphic encryption. We also introduce the first efficient andprivacy-preserving protocol for softmax function, which is an indispensablecomponent in CNNs and has not yet been evaluated in previous works due to itshigh complexity. We implemented the FALCON and evaluated the performance onreal-world CNN models. The experimental results show that FALCON outperformsthe best known works in both computation and communication cost.

FALCON: A Fourier Transform Based Approach for Fast and Secure Convolutional Neural Network Predictions