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Spatio-Temporal Features Based Sensitive Relationship Protection in Social Networks

  • Xiangyu Liu
  • Mandi Li
  • Xiufeng XiaEmail author
  • Jiajia Li
  • Chuanyu Zong
  • Rui Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11242)

Abstract

Without considering the spatio-temporal features of check-in data, there exists privacy leakage risk for the sensitive relationships in social networks. In this paper, a Spatio-Temporal features based Graph Anonymization algorithm (denoted as STGA) is proposed, in order to protect sensitive relationships in social networks. We firstly devise a relationship inference algorithm based on users’ neighborhoods and spatio-temporal features of check-in data. Then, in STGA, we propose an anonymizing method through suppressing the edges or check-in data to prevent the inference of sensitive relationships. We adopt a heuristic to obtain the inference secure graph with high data utility. A series of optimizing strategies are designed for the process of edge addition and graph updating, which reduce the information loss meanwhile improving the efficiency of the algorithm. Extensive experiments on real datasets demonstrate the practicality, high data utility and efficiency of our methods.

Keywords

Social network Sensitive relationship Privacy protection Spatio-temporal feature Data utility 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xiangyu Liu
    • 1
  • Mandi Li
    • 1
  • Xiufeng Xia
    • 1
    Email author
  • Jiajia Li
    • 1
  • Chuanyu Zong
    • 1
  • Rui Zhu
    • 1
  1. 1.School of Computer ScienceShenyang Aerospace UniversityShenyangChina

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