福建电脑2026，Vol.42Issue(3)：29-34,6.DOI:10.16707/j.cnki.fjpc.2026.03.006
多源用户画像差分隐私保护机制研究
A Differential Privacy Protection Mechanism for Multi-Source User Profiling
摘要
Abstract
To address privacy leakage risks in multi-source user profiling analysis,this paper proposes an end-to-end privacy protection framework based on differential privacy.The framework aggregates data at the user level,employs irreversible pseudonymization for identity protection,and integrates a persistent privacy budget management mechanism along with a pluggable extensible query engine.Experiments conducted on a real-world consumption dataset containing 8,636 users show that for the task of sum aggregation on consumption amounts,with a privacy budget set to 2.0,the average relative error is only 0.05%.In contrast,under the same scenario,the k-anonymity scheme(k=50)leads to a 0.4%data suppression rate and a 0.39%deterministic bias.The proposed framework enables secure statistical analysis of user profiling data,ensures data integrity,and provides a quantifiable privacy-utility trade-off superior to that of the k-anonymity approach.关键词
差分隐私/用户画像/隐私保护/数据安全/隐私预算Key words
Differential Privacy/User Profiling/Privacy Preservation/Data Security/Privacy Budget分类
信息技术与安全科学引用本文复制引用
徐东..多源用户画像差分隐私保护机制研究[J].福建电脑,2026,42(3):29-34,6.基金项目
本文得到陕西能源职业技术学院校级科研项目(No.2024KYZRPQN)资助. （No.2024KYZRPQN）
