Preserving weighted social networks privacy using random vectors perturbation technique

被引：0
作者：
Lan, Lihui [1 ,2 ]
Sui, Muheng [2 ]
Huang, Cheng [2 ]
机构：
[1] School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang
[2] School of Information Engineering, Shenyang University, Shenyang
来源：
Journal of Computational Information Systems | 2015年 / 11卷 / 16期
基金：
中国国家自然科学基金;
关键词：
Privacy preservation; Social networks; Vectors model; Weighted Euclidean distance;
D O I：
10.12733/jcis14853
中图分类号：
学科分类号：
摘要：
Due to needs of scientific research and data sharing, social networks should be released. To ensure individuals' sensitive information not to be leaked, privacy preservation should be implemented before publishing social networks. The existing methods tend to focus on un-weighted social networks for perturbing network structures or weighted social networks for protecting edge weights. Motivated by this, we present a random vectors perturbation method for protecting structures and edge weights of weighted social networks. The proposed method constructs vectors model of a weighted social network by segmentation based on vertices clustering using edge space theory. It can force attackers to re-identify in a large result set that the existential probabilities of vectors are same, and increase the uncertainty of recognition. The experimental results demonstrate that it can preserve the security of individuals' privacy, meanwhile it can protect some structure characteristics for social networks analysis and improve the utility of released social networks. © 2015 by Binary Information Press
引用
收藏
页码：5791 / 5798
页数：7
相关论文
共 17 条
  • [1] Hay M., Miklau G., Jensen D., Towsley D., Weis P., Resisting Structural Re-Identification in Anonymized Social Networks, Processing of Very Large Databases Endowment, 1, 1, pp. 102-114, (2008)
  • [2] Liu K., Terzi E., Towards Identity Anonymization on Graphs, Processing of ACM SIGMOD Int'l Conf. Management of Data, pp. 93-106, (2008)
  • [3] Zhou B., Pei J., Preserving Privacy in Social Networks against Neighborhood Attacks, Processing of IEEE 24th Int'l Conf. Data Eng., pp. 506-515, (2008)
  • [4] Hsu T., Liau C.J., Wang D.W., A logical framework for privacy-preserving social network publication, Journal of Applied Logic, 12, 2, pp. 151-174, (2014)
  • [5] Hay M., Miklau G., Jensen D., Weis P., Srivastava S., Anonymizing Social Networks, (2007)
  • [6] Ying X., Wu X., Randomizing Social Networks: A Spectrum Preserving Approach, Processing of Eighth SIAM Conf. Data Mining, pp. 739-750, (2008)
  • [7] Truta T.M., Campan A., Ralescu A.L., Preservation of structural properties in anonymized social networks, Processing of the 8th Collaborative Computing: Networking, Applications and Worksharing, pp. 619-627, (2012)
  • [8] Masoumzadeh A., Joshi J., Preserving structural properties in edge-perturbing anonymization techniques for social networks, IEEE Transactions on Dependable and Secure Computing, 9, 6, pp. 877-889, (2012)
  • [9] Das S., Egecioglu O., Abbadi A.E., Anónimos: An LP-Based Approach for Anonymizing Edge-Weighted Social Network Graphs, IEEE Transactions on Knowledge and Data Engineering, 24, 4, pp. 590-603, (2012)
  • [10] Liu L., Wang J., Liu J.Z., Privacy preservation in social networks with sensitive edge weights, Proceedings of SDM, pp. 949-960, (2009)
12