A Homomorphic-encryption-based Vertical Federated Learning Scheme for Rick Management

Wei Ou1, Jianhuan Zeng2, Zijun Guo1, Wanqin Yan1, Dingwan Liu1 and Stelios Fuentes3

  1. Department of Electronic and Information Engineering
    Hunan University of Science and Engineering, Yongzhou, China
    {ouwei1978430, yanwanqinqin, liudinwan}@163.com, GuoZijun0831@gmail.com
  2. Artificial Intelligence Research Center
    Qianhai Institute for Innovation Research, Shenzhen, China
  3. Leicester University


With continuous improvements of computing power, great progresses in algorithms and massive growth of data, artificial intelligence technologies have entered the third rapid development era. However, With the great improvements in artificial intelligence and the arrival of the era of big data, contradictions between data sharing and user data privacy have become increasingly prominent. Federated learning is a technology that can ensure the user privacy and train a better model from different data providers. In this paper, we design a vertical federated learning system for the for Bayesian machine learning with the homomorphic encryption. During the training progress, raw data are leaving locally, and encrypted model information is exchanged. The model trained by this system is comparable (up to 90%) to those models trained by a single union server under the consideration of privacy. This system can be widely used in risk control, medical, financial, education and other fields. It is of great significance to solve data islands problem and protect users’ privacy.

Key words

Data Security, Privacy Preservation, Federated Learning, EM Algorithm, Homomorphic Encryption

Digital Object Identifier (DOI)


Publication information

Volume 17, Issue 3 (October 2020)
Year of Publication: 2020
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Ou, W., Zeng, J., Guo, Z., Yan, W., Liu, D., Fuentes, S.: A Homomorphic-encryption-based Vertical Federated Learning Scheme for Rick Management. Computer Science and Information Systems, Vol. 17, No. 3, 819–834. (2020), https://doi.org/10.2298/CSIS190923022O