Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (4): 1160-1168.DOI: 10.11772/j.issn.1001-9081.2022030337

• Cyber security • Previous Articles    

Federated learning algorithm based on personalized differential privacy

Chunyong YIN(), Rui QU   

  1. School of Computer Science,Nanjing University of Information Science and Technology,Nanjing Jiangsu 210044,China
  • Received:2022-03-21 Revised:2022-05-13 Accepted:2022-05-25 Online:2023-04-11 Published:2023-04-10
  • Contact: Chunyong YIN
  • About author:QU Rui, born in 1999, M. S. candidate. His research interests include differential privacy, federated learning.


尹春勇(), 屈锐   

  1. 南京信息工程大学 计算机学院、软件学院、网络空间安全学院,南京 210044
  • 通讯作者: 尹春勇
  • 作者简介:屈锐(1999—),男,江苏宿迁人,硕士研究生,主要研究方向:差分隐私、联邦学习。


Federated Learning (FL) can effectively protect users' personal data from attackers. Differential Privacy (DP) is applied to enhance the privacy of FL, which can solve the problem of privacy disclose caused by parameters in the model training. However, existing FL methods based on DP on concentrate on the unified privacy protection budget and ignore the personalized privacy requirements of users. To solve this problem, a two-stage Federated Learning with Personalized Differential Privacy (PDP-FL) algorithm was proposed. In the first stage, the user's privacy was graded according to the user's privacy preference, and the noise meeting the user's privacy preference was added to achieve the purpose of personalized privacy protection. At the same time, the privacy level corresponding to the privacy preference was uploaded to the central aggregation server. In the second stage, in order to fully protect the global data, the simultaneous local and central protection strategy was adopted. And according to the privacy level uploaded by the user, the noise conforming to the global DP threshold was added to quantify the global privacy protection level. Experimental results show that on MNIST and CIFAR-10 datasets, the classification accuracy of PDP-FL algorithm reaches 93.8% to 94.5% and 43.4% to 45.2% respectively, which is better than those of Federated learning with Local Differential Privacy (LDP-Fed) algorithm and Federated Learning with Global Differential Privacy (GDP-FL) algorithm, PDP-FL algorithm meets the needs of personalized privacy protection.

Key words: Federated Learning (FL), Differential Privacy (DP), privacy preference, privacy rating, personalized privacy protection



关键词: 联邦学习, 差分隐私, 隐私偏好, 隐私分级, 个性化隐私保护

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