电讯技术2026，Vol.66Issue(1)：117-125,9.DOI:10.20079/j.issn.1001-893x.241127002
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
A Metric Local Differential Privacy-based Mechanism for Set-valued Data Privacy Protection in the Internet of Vehicles
摘要
Abstract
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.关键词
车联网/度量本地差分隐私/频率估计/集合型数据/隐私保护Key words
Internet of Vehicles/metric local differential privacy/frequency estimation/set-valued data/privacy protection分类
信息技术与安全科学引用本文复制引用
唐聪,薛乔,王箭,张焱..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[J].电讯技术,2026,66(1):117-125,9.基金项目
国家自然科学基金资助项目(62302214) （62302214）
国家自然科学基金联合基金重点项目(U2433205) （U2433205）
江苏省重点研发计划(产业前瞻与关键核心技术)项目(BE2022068,BE2022068-1) （产业前瞻与关键核心技术）
稳定支持国防特色学科基础研究项目(ILF240061A24) （ILF240061A24）
中国高校产学研创新基金——新一代信息技术创新项目课题(2023IT049) （2023IT049）
