Achieving Efficient and Privacy-Preserving Neural Network Training and Prediction in Cloud Environments

• Published in: IEEE transactions on dependable and secure computing Vol. 20; no. 5; pp. 1 - 12
• Main Authors: Zhang, Chuan, Hu, Chenfei, Wu, Tong, Zhu, Liehuang, Liu, Ximeng
• Format: Journal Article
• Language: English
• Published: Washington IEEE 01.09.2023; IEEE Computer Society
• Subjects: Additively homomorphic cryptosystem; Cloud computing; cloud environments; Computational modeling; Computer privacy; Cryptography; Data collection; Data models; data perturbation; Data privacy; Face recognition; Image processing; neural network; Neural networks; Perturbation; Prediction models; Predictions; Predictive models; Privacy; privacy-preserving; Training
• DOI: 10.1109/TDSC.2022.3208706
• ISSN: 1545-5971

The neural network has been widely used to train predictive models for applications such as image processing, disease prediction, and face recognition. To produce more accurate models, powerful third parties (e.g., clouds) are usually employed to collect data from a large number of users, which however may raise concerns about user privacy. In this paper, we propose an Efficient and Privacy-preserving Neural Network scheme, named EPNN, to deal with the privacy issues in cloud-based neural networks. EPNN is designed based on a two-cloud model and techniques of data perturbation and additively homomorphic cryptosystem. This scheme enables two clouds to cooperatively perform neural network training and prediction in a privacy-preserving manner and significantly reduces the computation and communication overhead among participating entities. Through a detailed analysis, we demonstrate the security of EPNN. Extensive experiments based on real-world datasets show EPNN is more efficient than existing schemes in terms of computational costs and communication overhead.