Incremental dynamic social network anonymity technology

被引：0
作者：
Guo C. [1 ]
Wang B. [1 ]
Zhu H. [1 ]
Yang X. [1 ]
机构：
[1] College of Information Science and Engineering, Northeastern University, Shenyang
来源：
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2016年 / 53卷 / 06期
基金：
高等学校博士学科点专项科研基金; 中国国家自然科学基金;
关键词：
Data privacy; Dynamic social network; Hypergraph; Incremental sequence; Loss of information; Weight list;
D O I：
10.7544/issn1000-1239.2016.20140695
中图分类号：
学科分类号：
摘要：
With the rapid development and popularity of social networks, how to protect privacy in social networks has become a hot topic in the realm of data privacy. However, in most of existing anonymity technologies in recent years, the social network is abstracted into a simple graph. In fact, there are a lot of incremental changes of social networks in real life and a simple graph can not reflect incremental society network well, so abstracting the social network into the incremental sequences becomes more realistic. Meanwhile, in real life most of the network contains weight information, that is, a lot of social networks are weighted graphs. Compared with the simple graph, weighted graphs carry more information of the social network and will leak more privacy. In this paper, incremental dynamic social network is abstracted into a weighted graph incremental sequence. In order to anonymize weighted graph incremental sequence, we propose a weighted graph incremental sequence k-anonymous privacy protection model, and design a baseline anonymity algorithm (called WLKA) based on weight list and HVKA algorithm based on hypergraph, which prevents the attacks from node point labels and weight packages. Finally, through a lot of tests on real data sets, it proves that WLKA can guarantee the validity of the privacy protection on weighted graph incremental sequence. Compared with WLKA, HVKA is better to retain the original structure properties and improves the availability of weight information, but it also reduces the time cost of anonymous process. © 2016, Science Press. All right reserved.
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页码：1352 / 1364
页数：12
相关论文
共 13 条
  • [1] Wang C.J.L., Wang E.T., Chen A.L.P., Anonymization for Multiple Released Social Network Graphs, Advances in Knowledge Discovery and Data Mining, pp. 99-110, (2013)
  • [2] Liu K., Terzi E., Towards identity anonymization on graphs, Proc of the 2008 ACM SIGMOD Int Conf on Management of Data, pp. 93-106, (2008)
  • [3] Zhou B., Pei J., Preserving privacy in social networks against neighborhood attacks, Proc of the 24th Int Conf on Data Engineering, pp. 506-515, (2008)
  • [4] Zou L., Chen L., Ozsu M.T., K-automorphism: A general framework for privacy preserving network publication, The VLDB Journal, 2, 1, pp. 946-957, (2009)
  • [5] Cheng J., Fu A.W., Liu J., K-isomorphism: Privacy preserving network publication against structural attacks, Proc of the 2010 ACM SIGMOD Int Conf on Management of Data, pp. 459-470, (2010)
  • [6] Yuan M., Chen L., Node protection in weighted social networks, Proc of Int Conf on Database Systems for Advanced Applications, pp. 123-137, (2011)
  • [7] Li Y., Shen H., Anonymizing graphs against weight-based attacks, Proc of the Int Conf on Data Mining Workshops (ICDMW), pp. 491-498, (2010)
  • [8] Liu X., Yang X., A generalization based approach for anonymizing weighted social network graphs, Proc of Int Conf on Web-Age Information Management, pp. 118-130, (2011)
  • [9] Yuan M., Chen L., Yu P.S., Personalized privacy protection in social networks, The VLDB Journal, 4, 2, pp. 141-150, (2010)
  • [10] Chen K., Liu X., Wang B., Et al., Anonymizing weighted social network graph against path-based attacks, Journal of Frontiers of Computer Science and Technology, 7, 11, pp. 961-972, (2013)
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