Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Resisting structural re-identification in anonymized social networks
Proceedings of the VLDB Endowment
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Personalized privacy protection in social networks
Proceedings of the VLDB Endowment
Neighborhood-privacy protected shortest distance computing in cloud
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Anonymizing shortest paths on social network graphs
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Data management challenges in cloud computing infrastructures
DNIS'10 Proceedings of the 6th international conference on Databases in Networked Information Systems
Degree Anonymization for K-Shortest-Path Privacy
SMC '13 Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics
Hi-index | 0.00 |
Efficient shortest path calculation has been studied extensively, in particular, in the distributed environment. However, preserving privacy in the cloud environment has just attracted latest attention. To preserve fixed-pattern one-neighborhood privacy in the cloud, current approach requires the calculation of all-pairs shortest paths in advance, which is time consuming for large graphs. In addition, specific paths that are sensitive and require hiding the source and destination vertices are not well addressed. In this work, we propose a new flexible k-neighborhood privacy-protection and efficient shortest distance computation scheme for sensitive shortest paths in the cloud environment. Combining the construction of k-skip shortest path sub-graphs, sensitive vertex adjustment, vertex hierarchy labeling and bottom-up partitioning techniques, the proposed approach not only subsumes one-neighborhood privacy but also provides efficient partitioning and query processing for sensitive shortest paths. Numerical experiments demonstrating the characteristics of proposed approach are presented.