Privacy-Preserving Deep Sequential Model with Matrix Homomorphic Encryption

Privacy-Preserving Deep Sequential Model with Matrix Homomorphic Encryption

연구 분야: Cryptography

학회: ASIA CCS '22: Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security

Making deep neural networks available as a service introduces privacy problems, for which homomorphic encryption of both model and user data potentially offers the solution at the highest privacy level. However, the difficulty of operating on homomorphically encrypted data has hitherto limited the range of operations available and the depth of networks. We introduce an extended CKKS scheme MatHEAAN to provide efficient matrix representations and operations together with improved noise control. Using the MatHEAAN we developed a deep sequential model with a gated recurrent unit called MatHEGRU. We evaluated the proposed model using sequence modeling, regression, and classification of images and genome sequences. We show that the hidden states of the encrypted model, as well as the results, are consistent with a plaintext model.