Optimizing Query Times for Multiple Users Scenario of Differential Privacy
Wen Cai Huang, Shijie Zhou, Yongjian Liao, Ming Zhuo · IEEE Access · 2019
Differential privacy is the state-of-the-art for preserving privacy and differential privacy mechanism based on Laplace distribution with mean 0 is common practice. However, privacy budget is exhausted so quick that the number of queries is not enough. In this paper, a differential privacy mechanism is proposed to optimize the number of queries for application scenario of multiple users. We isolate different users by assigning various noise distribution with non-zero mean to different users. First, in terms of privacy guarantee, the proposed mechanism is better than common practice. Second, for the utility aspect, the accuracy of proposed mechanism is analyzed from the view of data distribution's distortion and the view of noise's absolute value.