南京邮电大学学报（自然科学版）2026，Vol.46Issue(3)：14-22,9.DOI:10.14132/j.cnki.1673-5439.2026.03.002
基于输入区别本地差分隐私的键值数据收集机制
A key-value data collection mechanism based on input-discriminative local differential privacy
摘要
Abstract
Local differential privacy(LDP)provides quantifiable privacy preservation for data collection without relying on a trusted third party.In real-world scenarios,key-value data,as typical non-relational data,usually exhibits diverse sensitivities across different keys.However,traditional LDP frameworks only enforce the same level of privacy protection for all key-value data.To address this limitation,this pa-per proposes a key-value data collection mechanism based on input discriminative local differential pri-vacy(ID-LDP)to accommodate personalized privacy requirements for different data items.First,we present the formal definition of ID-LDP and clarify its differences from conventional LDP.Second,we for-mulate the target optimization problem and identify the corresponding challenges.Third,we design a lo-cal perturbation mechanism and a server aggregation mechanism,and derive a method to compute utility-optimized perturbation probabilities.Finally,theoretical analysis and extensive experimental results dem-onstrate the superiority of the proposed mechanism over traditional approaches.关键词
本地差分隐私/输入区别隐私保护/键值数据/频率估计/均值估计Key words
local differential privacy/input-discriminative privacy protection/key-value date/fre-quency estimation/mean estimation分类
信息技术与安全科学引用本文复制引用
陈曦贤,朱友文,吴启晖..基于输入区别本地差分隐私的键值数据收集机制[J].南京邮电大学学报（自然科学版）,2026,46(3):14-22,9.基金项目
国家自然科学基金(U2433205)和江苏省重点研发计划(产业前瞻与关键核心技术)(BE2022068,BE2022068-1)资助项目 （U2433205）
