车联网中基于度量本地差分隐私的集合数据隐私保护机制OA

Existing privacy protection mechanisms for set-valued data,if directly applied to the Internet of Vehicles(IoV),can significantly impact the accuracy of frequency distribution estimation results.To address this shortcoming,a symmetric difference private set(SDPrivSet)protocol based on the metric local differential privacy model is proposed.In SDPrivSet,users locally perturb data before submitting them to the server,which then estimates the true frequency distribution based on the received perturbed data.This protocol provides strict data privacy protection,has low computational overhead on both the user and server sides,and maintains high data utility during statistical analysis.Experimental results on real datasets show that the SDPrivSet protocol performs optimally under any original data domain,set size,and privacy budget.Compared with existing protocols,it enhances performance by at least 34.20%,with more significant performance improvements when the set size and privacy budget are larger.