Collecting Multidimensional Numerical Data and Estimating Mean with Personalized Local Differential Privacy

Abstract

Collecting and analyzing data can generate a wealth of knowledge, but it can also raise privacy concerns. Local differential privacy (LDP) is the latest privacy standard to address this issue and has been implemented on platforms such as Chrome, iOS, and macOS. In the LDP solution, users first perturb their own data on the user side and then upload the perturbed data to the server. This not only protects against background attacks but also against untrusted servers. However, existing multidimensional solutions ignore the personalized privacy needs of users. In this paper, we meet the personalized privacy needs of users while reducing the mean square error of the perturbed data. Specifically, we first designed a personalized privacy budget allocation within a certain range, which meets the personalized privacy needs of users. Then, we optimized the sampling dimension of the existing solution, which resulted in a smaller mean square error of the perturbed data. Finally, we proposed our solution for collecting multidimensional numerical data and estimating the mean. In addition, we conducted experiments on two real datasets. The results demonstrate that the mean square error of our solution is lower than the existing solutions.