信息安全研究2025，Vol.11Issue(8)：736-745,10.DOI:10.12379/j.issn.2096-1057.2025.08.07
差分隐私洗牌模型在范围查询中的应用研究
Application Research of Differential Privacy Shuffle Model in Range Query
摘要
Abstract
Range queries are key indicators in data analysis under various scenarios.However,when dealing with individual-level data,personal privacy issues will be involved.To address this problem,range query protocols that meet local differential privacy(LDP)have been proposed.These protocols enable data collectors to collect aggregated information about the population without relying on trusted third parties while protecting the privacy of each user.Nevertheless,the perturbation methods used in the existing range query protocols based on LDP have limitations,which restrict their effectiveness.In addition,these protocols usually exhibit poor estimation performance for small range intervals.In light of this,a Hierarchical Range Query protocol based on the differential privacy shuffling model(SHRQ)is proposed.Firstly,this paper extensively analyzes the variance of the perturbation methods in previous protocols.The SHRQ protocol selects the optimal perturbation method according to the number of nodes in each layer.Then,the SHRQ makes the most of the advantages of the shuffling model by leveraging prior knowledge from the previous round for multiple iterations,significantly improving the estimation accuracy of small range query intervals.Through extensive comparative experiments on both simulated and real-world datasets,it is demonstrated that after a few iterations,SHRQ reduces the estimation error for small ranges by an order of magnitude and for large ranges by half an order of magnitude compared to previous protocols.关键词
差分隐私/范围查询/隐私保护/洗牌模型/数据安全Key words
differential privacy/range query/privacy protection/shuffle model/data security分类
信息技术与安全科学引用本文复制引用
王梓行,陈兵..差分隐私洗牌模型在范围查询中的应用研究[J].信息安全研究,2025,11(8):736-745,10.基金项目
国家重点研发计划项目(2022YFC3301700) （2022YFC3301700）
