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Common Neighborhood Estimation over Bipartite Graphs under Local Differential Privacy

Summary: Estimates common neighbors in bipartite graphs under edge LDP. Proposes a multi-round pruning framework with unbiased local estimators, leveraging neighbors of both query vertices, optimized privacy-budget allocation boosts accuracy under degree imbalance. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7039
Venue
SIGMOD
Year
2024
Pagerank
5.2528121e-05
Overall Rank
9,557 | 34.44%
DOI
10.1145/3698803

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{he_sigmod24,
        title = {{Common Neighborhood Estimation over Bipartite Graphs under Local Differential Privacy}},
        author = {He, Yizhang and Wang, Kai and Zhang, Wenjie and Lin, Xuemin and Zhang, Ying},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3698803},
        url = {https://dl.acm.org/doi/10.1145/3698803},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,442 Defense against Poisoning Attacks under Shuffle-DP 2026 SIGMOD 5.093636e-05
10,446 Efficient and Effective Biclique Counting with Local Differential Privacy 2026 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

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

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