A verifiable and secure DNN classification model over encrypted data
Yajun Wang, Guanghui Sun, Weixun Li, Long Yuan, Minghui Gao, Yan Dong, Chen Wang · International Journal of Computational Science and Engineering · 2024
Outsourcing deep neural networks (DNN) is beneficial to reduce the client overhead, but there are sensitive data privacy issues. However, the existing schemes not only fall short in privacy-preserving and gradient integrity verification, but also fail to perform complex nonlinear operations. In this paper, we design, implement, and evaluate a verifiable and secure DNN classification model over encrypted data (DNNCM-ED), which provides confidentiality and integrity verification simultaneously. Firstly, we propose a new framework in which the client and model training servers jointly train the DNN model to achieve the model training server for aggregation. Secondly, we design secure communication protocols for basic operations, which can be used to construct DNN classification model. Finally, we further devise a verifiable algorithm related to the DNNCM-ED, which provides confidentiality and integrity verification simultaneously. Extensive property and performance analyses indicate that DNNCM-ED is effective, as well as sharing communication and computation overhead of the cloud.