Multi-Dimensional Data Collection Under Personalized Local Differential Privacy

This work addresses the problem of collecting multidimensional data while adhering to personalized local differential privacy. In this local context, each user possesses a data record containing multiple attributes. Due to variations in attribute sensitivity, privacy requirements of the attributes differ. Balancing personalized privacy needs of attributes with maximizing the accuracy of statistical results presents a significant challenge. The accuracy of estimation results is closely tied to the allocation of privacy budgets and user grouping. Consequently, we define an optimization problem: determining the allocation of privacy budgets to each attribute within the group and identifying the corresponding number of users in the group to ensure optimal estimation result accuracy while meeting privacy constraints. To simplify the complexity of this optimization problem, we introduce a progressive optimization method. This method initially groups attributes and subsequently fine-tunes and optimizes them. Practically, our approach meets the personalized privacy protection requirements of attributes. Experimental results indicate that our approach achieves significant improvements in data utility.