Support Personalized Weighted Local Differential Privacy Skyline Query

Abstract

The potential privacy risks in certain situations are of concern because of the frequent sharing of data during skyline queries, leading to leakage of users’ private information. The most common privacy-preserving technique is to anonymize data by removing or changing certain information, for which an attack with specific background knowledge would render the privacy protection ineffective. To overcome these difficulties, this study proposes a personalized weighted local differential privacy method (PWLDP) to protect data privacy during skyline querying. Compared with existing studies of skyline queries under privacy protection, the degree of privacy protection can be quantitatively analyzed, and the processing of data privacy lies with the user, who quantitatively perturbs the processing according to the sensitivity of the weights of different attributes to avoid substantial information loss. The performance of the proposed PWLDP is verified by comparing PWLDP and LDP on different datasets, the average privacy leakage reduction of 62.22% and 51.67% is obtained for experiments conducted on different datasets relative to the iDP-SC algorithm, and the experimental results demonstrate the efficiency and advantages of the proposed method.