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Neighborhood-Privacy Protected Shortest Distance Computing in Cloud

Summary: Neighborhood-privacy for cloud-based shortest-distance queries by splitting a graph G into a local link graph Gl and outsourced graphs Go under a novel 1-neighborhood-d-radius model, preventing neighborhood attacks while preserving distances. Greedy Gl/Go construction minimizes client storage for exact answers; plus an efficient transformation supports additive-error approximate distances, with empirical validation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4462
Venue
SIGMOD
Year
2011
Pagerank
6.1924452e-05
Overall Rank
5,507 | 62.22%
DOI
10.1145/1989323.1989367

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{gao_sigmod11,
        title = {{Neighborhood-Privacy Protected Shortest Distance Computing in Cloud}},
        author = {Gao, Jun and Xu, Jeffery Yu and Jin, Ruoming and Zhou, Jiashuai and Wang, Tengjiao and Yang, Dongqing},
        series = {{SIGMOD} '11},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1989323.1989367},
        url = {https://dl.acm.org/doi/10.1145/1989323.1989367},
        year = {2011}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
2,874 iBFS: Concurrent Breadth-First Search on GPUs 2016 SIGMOD 8.0094569e-05
5,384 Privacy Preserving Subgraph Matching on Large Graphs in Cloud 2016 SIGMOD 6.2370509e-05
7,817 Shortest Path Computation with No Information Leakage 2012 VLDB 5.5377369e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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