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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)

Abstract

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.

Keywords

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

Notes

Acknowledgments

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

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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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