基于边介数模型的差分隐私保护方案

黄海平; 王凯; 汤雄; 张东军

doi:10.11959/j.issn.1000-436x.2019095

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基于边介数模型的差分隐私保护方案

学术论文 | 更新时间：2024-06-05

- 基于边介数模型的差分隐私保护方案
- Differential privacy protection scheme based on edge betweenness model
- 通信学报 2019年40卷第5期页码：88-97
- 作者机构：
  
  1. 南京邮电大学计算机学院，江苏南京 210023
  2. 江苏省无线传感网高技术研究重点实验室，江苏南京 210023
- 作者简介：
  
  [ "黄海平（1981- ），男，福建三明人，博士，南京邮电大学计算机学院教授、副院长，主要研究方向为物联网安全和数据隐私保护等。" ]
  [ "王凯（1994- ），男，江苏扬州人，南京邮电大学硕士生，主要研究方向为物联网安全和数据隐私保护。" ]
  [ "汤雄（1992- ），男，江苏沭阳人，南京邮电大学硕士生，主要研究方向为物联网安全和数据隐私保护。" ]
  [ "张东军（1993- ），男，江苏徐州人，南京邮电大学硕士生，主要研究方向为物联网安全和数据隐私保护。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61672297);江苏省重点研发计划（社会发展）基金资助项目(BE2017742);江苏省六大人才高峰基金资助项目(DZXX-017)
- DOI：10.11959/j.issn.1000-436x.2019095
  中图分类号： TP309
- 网络出版日期：2019-05，
  
  纸质出版日期：2019-05-25
- 稿件说明：
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黄海平, 王凯, 汤雄, 等. 基于边介数模型的差分隐私保护方案[J]. 通信学报, 2019,40(5):88-97.

Haiping HUANG, Kai WANG, Xiong TANG, et al. Differential privacy protection scheme based on edge betweenness model[J]. Journal on communications, 2019, 40(5): 88-97.
黄海平, 王凯, 汤雄, 等. 基于边介数模型的差分隐私保护方案[J]. 通信学报, 2019,40(5):88-97. DOI： 10.11959/j.issn.1000-436x.2019095.

Haiping HUANG, Kai WANG, Xiong TANG, et al. Differential privacy protection scheme based on edge betweenness model[J]. Journal on communications, 2019, 40(5): 88-97. DOI： 10.11959/j.issn.1000-436x.2019095.

摘要

随着社交网络应用的不断发展，用户社交关系等个人隐私数据的安全保护问题亟待解决。为显著减小社交网络数据的敏感度，提出了一种基于边介数模型的差分隐私保护方案 BCPA。基于 dK 模型捕获图结构对应的2K 序列，根据边中介中心性系数对 2K 序列重新排序；依据排序结果将 2K 序列聚类成多个子序列，再利用 dK扰动算法对各子序列分别进行加噪；根据整合后的新 2K 序列生成满足差分隐私的社交网络发布图。基于真实数据集，通过模拟仿真将所提方案与其他经典方案进行比较，实验结果表明，所提方案在保证较强隐私保护性的同时，提高了发布数据的准确性和可用性。

Abstract

With the continuous development of social network application

user’s personal social data is so sensitive that the problem of privacy protection needs to be solved urgently.In order to reduce the network data sensitivity

a differential privacy protection scheme BCPA based on edge betweenness model was proposed.The 2K sequence corresponding to the graph structure based on the dK model was captured

and 2K sequences based on the edge betweenness centrality were reordered.According to the result of reordering

the 2K sequence was grouped into several sub-sequences

and each sub-sequence was respectively added with noise by a dK perturbation algorithm.Finally

a social network graph satisfying differential privacy was generated according to the new 2K sequences after integration.Based on the real datasets

the scheme was compared with the classical schemes through simulation experiments.The results demonstrate that it improves the accuracy and usability of data while ensuring desired privacy protection level.

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references

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