Improving Laplace Mechanism of Differential Privacy by Personalized Sampling
Wen Cai Huang, Shijie Zhou, Tianqing Zhu, Yongjian Liao, Chunjiang Wu, Shilin Qiu · 2020
The differential privacy is the state-of-the-art conception for privacy preservation due to its strong privacy guarantees, however it suffers from low accuracy. In this paper, we propose a personalized sample Laplace mechanism by combining the Laplace mechanism with sampling technology. In order to improve the accuracy, the proposed mechanism assigns personalized sampling probability to each record. Based on the personalized sampling probability, we prove that the proposed mechanism satisfies ε differential privacy. Then we compare the proposed mechanism with other mechanisms in term of the accuracy. Through extensive experiments on synthetic data set and real world data set, we demonstrate that the performance of proposed mechanism is better.