期刊
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
卷 15, 期 -, 页码 1486-1500出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIFS.2019.2939713
关键词
Collaborative learning; deep learning; privacy
资金
- NSFC [61822207, U1636219, 61872277, 41571437, 61702380]
- Equipment Pre-Research Joint Fund of Ministry of Education of China (Youth Talent) [6141A02033327]
- Outstanding Youth Foundation of Hubei Province [2017CFA047]
- Fundamental Research Funds for the Central Universities [2042019kf0210]
- Natural Science Foundation of Hubei Province [2018CFB482, 2017CFB134]
- Hubei Provincial Technological Innovation Special Funding Major Projects [2017AAA125]
With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived great success in many applications such as image classification, speech recognition and machine translation etc. While deep learning has been increasingly popular, the problem of privacy leakage becomes more and more urgent. Given the fact that the training data may contain highly sensitive information, e.g., personal medical records, directly sharing them among the users (i.e., participants) or centrally storing them in one single location may pose a considerable threat to user privacy. In this paper, we present a practical privacy-preserving collaborative deep learning system that allows users to cooperatively build a collective deep learning model with data of all participants, without direct data sharing and central data storage. In our system, each participant trains a local model with their own data and only shares model parameters with the others. To further avoid potential privacy leakage from sharing model parameters, we use functional mechanism to perturb the objective function of the neural network in the training process to achieve epsilon-differential privacy. In particular, for the first time, we consider the existence of unreliable participants, i.e., the participants with low-quality data, and propose a solution to reduce the impact of these participants while protecting their privacy. We evaluate the performance of our system on two well-known real-world datasets for regression and classification tasks. The results demonstrate that the proposed system is robust against unreliable participants, and achieves high accuracy close to the model trained in a traditional centralized manner while ensuring rigorous privacy protection.
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