邻居子图扰动下的
k -度匿名隐私保护模型暂无标题
- 西安电子科技大学学报 2023年50卷第4期 页码：180-193
- 作者机构：
1. 贵州财经大学 信息学院 贵州省大数据统计分析重点实验室,贵州 贵阳 550025
2. 贵安新区科创产业发展有限公司,贵州 贵阳 550025
- 作者简介：
[ "丁红发(1988—),男,副教授,E-mail:[email protected];" ]
[ "唐明丽(1998—),男,贵州财经大学硕士研究生,E-mail:[email protected];" ]
[ "刘海(1984—),男,副教授,E-mail:[email protected];" ]
[ "蒋合领(1983—),男,讲师,E-mail:[email protected];" ]
[ "傅培旺(2000—),男,贵州财经大学硕士研究生,E-mail:[email protected];" ]
[ "于莹莹(1998—),女,贵州财经大学硕士研究生,E-mail:[email protected]" ]
- 基金信息：国家自然科学基金(62002080);贵州财经大学校级在校生科研项目(2022ZXSY167)
DOI：10.19665/j.issn1001-2400.2023.04.018
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丁红发, 唐明丽, 刘海, 等. 邻居子图扰动下的
k -度匿名隐私保护模型[J]. 西安电子科技大学学报, 2023,50(4):180-193.丁红发, 唐明丽, 刘海, 等. 邻居子图扰动下的
k -度匿名隐私保护模型[J]. 西安电子科技大学学报, 2023,50(4):180-193. DOI： 10.19665/j.issn1001-2400.2023.04.018.
摘要
大规模图数据在商业和学术研究中应用广泛,在其共享发布场景中隐私保护极为重要。现有的匿名隐私保护模型难以有效解决图数据隐私保护和数据效用间的冲突问题。针对此问题,基于邻居子图扰动提出一种增强隐私保护程度和数据效用水平的,k,度匿名隐私保护模型。首先,该模型利用邻居子图扰动机制优化扰动图数据节点的1-邻居子图,提高扰动效率并减少数据效用损失;其次,利用分治策略并依据节点度序列实现对节点匿名组的优化划分,提高匿名图数据的效用;最后,采用边修改和子图边缘修改的策略重构匿名图数据,实现图数据,k,度匿名隐私保护。对比和实验结果表明,所提出模型比现有模型在计算开销和安全性方面有了较大提升,能够同时抗节点度攻击和邻居子图攻击,在边变化比例、信息损失、平均节点度变化和聚类系数等指标方面数据效用显著提升。
Abstract
With the increasing use of mass graph data in commerce and academia,it has become critical to ensure privacy when sharing and publishing graph data.However,existing anonymous privacy-preserving models struggle to balance the conflict between privacy and utility of graph data.To address this issue,a ,k,-degree anonymity privacy preserving model based on neighbor subgraph perturbation has been proposed,which enhances both the levels of privacy preservation and data utility.To achieve ,k,-degree anonymity privacy preservation,this model first perturbs the 1-neighbor subgraph of each node in graph data by using neighbor subgraph perturbation.This perturbation is optimized,resulting in improved perturbing efficiency and reduced data utility loss.Next,the partition of anonymous node group is optimized by using a divide-and-conquer strategy based on the degree sequence of nodes,leading to improved utility of the anonymized graph.Finally,the anonymized graph is reconstructed by editing both edges and subgraph borders to achieve ,k,-degree anonymity privacy preservation.Comparisons and experiments have shown that the proposed model greatly improves both the overhead and security when compared to existing models and that it is able to resist both degree-based attacks and neighborhood attacks.Furthermore,the data utility is greatly improved,as evidenced by metrics such as change proportion of edges,information loss,change in the average degree of nodes,and clustering coefficient.
关键词
隐私保护技术图结构匿名k-度匿名邻居子图
Keywords
privacy-preserving techniquesgraph structuresanonymizationk-degree anonymousneighbor subgraph
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