K-Neighborhood Shortest Path Privacy in the Cloud

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


Preserving privacy on various forms of published data has been studied extensively in recent years. In particular, shortest distance computing in the cloud, while maintaining neighborhood privacy, attracts latest attention. To preserve fixed-pattern one-neighborhood privacy, current approach requires the calculation of all-pairs shortest paths in advance, which is time consuming for large graphs. In this work, we propose a new flexible k-neighborhood privacy-protected and efficient shortest distance computation scheme in the cloud. Combining k-skip shortest path sub-graphs, vertex hierarchy labeling and bottom-up partitioning, the proposed technique not only subsumes one-neighborhood privacy but also provides efficient partitioning and query processing. Numerical experiments demonstrating the characteristics of proposed approach are presented.


Privacy preservation k-neighborhood privacy Shortest path distance k-skip 



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


  1. 1.
    Agrawal D, El Abbadi A, Antony S, Das S (2010) Data management challenges in cloud computing infrastructures. In: Proceedings of the 6th international conference on databases in networked information systems, Berlin, pp 1–10Google Scholar
  2. 2.
    Das S, Egecioglu O, El Abbadi A (2010) Anonymizing weighted social network graphs. In: 2010 IEEE 26th international conference on data engineering (ICDE), pp 904–907Google Scholar
  3. 3.
    Fu AW-C, Wu H, Cheng J, Chu S, Wong RC-W (2012) IS-LABEL: an independent-set based labeling scheme for point-to-point distance querying on large graphs. arXiv:1211.2367Google Scholar
  4. 4.
    Gao J, Yu JX, Jin R, Zhou J, Wang T, Yang D (2011) Neighborhood-privacy protected shortest distance computing in cloud. In: Proceedings of the 2011 ACM SIGMOD international conference on management of data, New York, pp 409–420Google Scholar
  5. 5.
  6. 6.
    Liu L, Liu J, Zhang J (2010) Privacy preservation of affinities in social networks. In: ICISGoogle Scholar
  7. 7.
    Liu L, Wang J, Liu J, Zhang J (2009) Privacy preservation in social networks with sensitive edge weights. In: SDM, pp 954–965Google Scholar
  8. 8.
    Wang SL, Shih CC, Ting HH, Hong TP (2013) Degree anonymization for K-shortest-path privacy. In: IEEE international conference on SMC, Manchester, (submitted)Google Scholar
  9. 9.
    Wang SL, Tsai ZZ, Hong TP, Ting HH (2011) Anonymizing shortest paths on social network graphs. In: The third asian conference on intelligent information and database systems (ACIIDS), DaeguGoogle Scholar
  10. 10.
  11. 11.
    Tao Y, Sheng C, Pei J (2011) On k-skip shortest paths. In: Proceedings of the 2011 ACM SIGMOD international conference on management of data, New York, pp 421–432Google Scholar
  12. 12.

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Shyue-Liang Wang
    • 1
    Email author
  • Jia-Wei Chen
    • 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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