Edge Selection for Degree Anonymization on K Shortest Paths

  • Shyue-Liang WangEmail author
  • Ching-Chuan Shih
  • I-Hsien Ting
  • Tzung-Pei Hong
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


Privacy preserving network publishing has been studied extensively in recent years. Although more works have adopted un-weighted graphs to model network relationships, weighted graph modeling can provide deeper analysis of the degree of relationships. Previous works on weighted graph privacy have concentrated on preserving the shortest path characteristic between pairs of vertices. Two common types of privacy have been proposed. One type of privacy tried to add random noise edge weights to the graph but still maintain the same shortest path. The other privacy, k-shortest path privacy, minimally perturbed edge weights so that there exist k shortest paths. However, the k-shortest path privacy did not consider degree attacks on the nodes of anonymized shortest paths. For example, if the adversary possesses background knowledge of node degrees on the shortest path, the true shortest path can be identified. We have previously presented a new concept called (k 1 , k 2 )-shortest path privacy to prevent such privacy breach [1]. A published network graph with (k 1 , k 2 )-shortest path privacy has at least k 1 indistinguishable shortest paths between the source and destination vertices. In addition, for the non-overlapping vertices on the k 1 shortest paths, there exist at least k 2 vertices with same node degree and lie on more than one shortest path. In this work, we further propose edge insertion and edge weight determination techniques to effectively achieve the proposed privacy. Numerical comparisons based on average clustering coefficient and average shortest path length show that the proposed TNF approach is simple and effective.


Social networks Privacy preserving Edge weights K-shortest path privacy (K1, K2)-shortest path privacy 



This work was supported in part by the National Science Council, Taiwan, under grant NSC 101–2221-E-390–028-MY3.


  1. 1.
    Wang SL, Shih CC, Ting HH, Hong TP (2013) Degree anonymization for k-shortest-path privacy, submitted to 2013. IEEE international conference on SMC, Manchester, October 2013Google Scholar
  2. 2.
    Government Information Laws.
  3. 3.
    Cheng J, Fu A, Liu J (2010) K-isomorphism: privacy preserving network publication against structural attacks. In: SIGMOD conference, 459–470Google Scholar
  4. 4.
    Das S, Egecioglu O, Abbadi AE (2010) Anonymizing weighted social network graphs. In: ICDE, 904–907Google Scholar
  5. 5.
    Wang SL, Tsai YC, Kao HY, Hong TP (2010) Anonymizing set-valued social data. The 2010 international symposium on social computing and networking (SocialNet’10), Hangzhou, December 2010Google Scholar
  6. 6.
    Wang SL, Tsai ZZ, Hong TP, Ting HH (2011) Anonymizing shortest paths on social network graphs. The 3rd Asian conference on intelligent information and database systems (ACIIDS), Daegu, April 2011Google Scholar
  7. 7.
    Zhou B, Pei J (2008) Preserving privacy in social networks against neighborhood attacks. In: ICDE, 506–515Google Scholar
  8. 8.
    Zou L, Chen L, Ozsu MT (2009) K-automorphism: A general framework for privacy preserving network publication. In VLDB, 200Google Scholar
  9. 9.
    Liu L, Liu J, Zhang J (2010) Privacy preservation of affinities in social networks. In: ICISGoogle Scholar
  10. 10.
    Liu L, Wang J, Liu J, Zhang J (2009) Privacy preservation in social networks with sensitive edge weights. In: SDM, 954–965Google Scholar
  11. 11.
    LINQS, Statistical relational learning group at University of Maryland, USA,
  12. 12.
    Liu K, Terzi E (2008) Towards identity anonymization on graphs. In: SIGMOD Conference, 93–106Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Shyue-Liang Wang
    • 1
    Email author
  • Ching-Chuan Shih
    • 1
  • I-Hsien Ting
    • 1
  • Tzung-Pei Hong
    • 2
  1. 1.Department of Information ManagementNational University of KaohsiungKaohsiungTaiwan
  2. 2.Department of Computer Science and Information EngineeringNational University of KaohsiungKaohsiungTaiwan

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