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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
4400
Venue
SIGMOD
Year
2011
Pagerank
5.4813218e-05
Overall Rank
5,485 | 61.85%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
3,233 iBFS: Concurrent Breadth-First Search on GPUs 2016 SIGMOD 7.3361904e-05
4,940 Privacy Preserving Subgraph Matching on Large Graphs in Cloud 2016 SIGMOD 5.8180285e-05
7,447 Shortest Path Computation with No Information Leakage 2012 VLDB 4.7273556e-05
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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