NeurIPS 2020

AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference

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The paper describes a new method for selecting Homomorphic Encryption scheme parameters used for Linear layers in a secure inference setting. The paper explores an interesting combination of the particulars of homomorphic encryption and deep neural networks and is able to provide strong decryption correctness guarantees.