PLDP: Personalized Local Differential Privacy for Multidimensional Data Aggregation
Zixuan Shen, Zhihua Xia, Peipeng Yu
Abstract
The collection of multidimensional crowdsourced data has caused a public concern because of the privacy issues. To address it, local differential privacy (LDP) is proposed to protect the crowdsourced data without much loss of usage, which is popularly used in practice. However, the existing LDP protocols ignore users’ personal privacy requirements in spite of offering good utility for multidimensional crowdsourced data. In this paper, we consider the personality of data owners in protection and utilization of their multidimensional data by introducing the notion of personalized LDP (PLDP). Specifically, we design personalized multiple optimized unary encoding (PMOUE) to perturb data owners’ data, which satisfies <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:msub> <a:mrow> <a:mi>ϵ</a:mi> </a:mrow> <a:mrow> <a:mtext>total</a:mtext> </a:mrow> </a:msub> </a:math> -PLDP. Then, the aggregation algorithm for frequency estimation on multidimensional data under PLDP is developed, which is described in two situations. Experiments are conducted on four real datasets, and the results show that the proposed aggregation algorithm yields high utility. Moreover, case studies with four real datasets demonstrate the efficiency and superiority of the proposed scheme.