In this paper, we propose new ideas to protect user privacy while allowing the use of a user history graph. We define new privacy notions for user history graphs and consider algorithms to generate a privacy-preserving digraph from the original graph.


  1. 1.
    Arai, H., Sakuma, J.: Privacy Preserving Semi-supervised Learning for Labeled Graphs. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part I. LNCS, vol. 6911, pp. 124–139. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Blaustein, B., Chapman, A., Seligman, L., Allen, M.D., Rosenthal, A.: Surrogate parenthood: Protected and informative graphs. In: Proc. of the 37th International Conference on Very Large Data Bases, VLDB 2011, pp. 518–527 (2011)Google Scholar
  3. 3.
    Hay, M., Li, C., Miklau, G., Jensen, D.: Accurate estimation of the degree distribution of private networks. In: Proc. of the 2009 9th IEEE International Conference on Data Mining, ICDM 2009, pp. 169–178 (2009)Google Scholar
  4. 4.
    Heer, J., Mackinlay, J., Stolte, C., Agrawala, M.: Graphical histories for visualization: Supporting analysis, communication, and evaluation. IEEE Transactions on Visualization and Computer Graphics 14(6), 1189–1196 (2008)CrossRefGoogle Scholar
  5. 5.
    Karwa, V., Raskhodnikova, S., Smith, A., Yaroslavtsev, G.: Private analysis of graph structure. In: Proc. of the 37th International Conference on Very Large Data Bases, VLDB 2011, pp. 1146–1157 (2011)Google Scholar
  6. 6.
    Kurashima, T., Bessho, K., Toda, H., Uchiyama, T., Kataoka, R.: Ranking Entities Using Comparative Relations. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds.) DEXA 2008. LNCS, vol. 5181, pp. 124–133. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Rastogi, V., Hay, M., Miklau, G., Suciu, D.: Relationship privacy: output perturbation for queries with joins. In: Proc. of the 28th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2009, pp. 107–116 (2009)Google Scholar
  8. 8.
    Sakuma, J., Kobayashi, S.: Link analysis for private weighted graphs. In: Proc. of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 235–242 (2009)Google Scholar
  9. 9.
    Samarati, P., Sweeney, L.: Generalizing data to provide anonymity when disclosing information. In: Proc. of the 17th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS 1998), p. 188 (1998)Google Scholar
  10. 10.
  11. 11.
    Weinberg, Z., Chen, E.Y., Jayaraman, P.R., Jackson, C.: I still know what you visited last summer: Leaking browsing history via user interaction and side channel attacks. In: 2011 IEEE Symposium on Security and Privacy, pp. 147–161 (2011)Google Scholar
  12. 12.
    Ying, X., Wu, X.: Randomizing social networks: a spectrum preserving approach. In: Proc. of the 8th SIAM Conference on Data Mining, SDM 2008, pp. 739–750 (2008)Google Scholar
  13. 13.
    Yuan, M., Chen, L., Yu, P.S.: Personalized privacy protection in social network. In: Proc. of the 37th International Conference on Very Large Data Bases, VLDB 2011, pp. 141–150 (2011)Google Scholar
  14. 14.
    Zhou, B., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: Proc. of the 24th International Conference on Data Engineering, ICDE 2008, pp. 506–515 (2008)Google Scholar
  15. 15.
    Zou, L., CHen, L., Özsu, M.T.: k-automorphism: a general framework for privacy preserving network publication. In: Proc. of the 35th International Conference on Very Large Data Bases, VLDB 2009, pp. 946–957 (2009)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Shinsaku Kiyomoto
    • 1
  • Kazuhide Fukushima
    • 1
  • Yutaka Miyake
    • 1
  1. 1.KDDI R & D Laboratories Inc.FujiminoJapan

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