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DPGraph: A Benchmark Platform for Differentially Private Graph Analysis

Summary: DPGraph offers a web-based, end-to-end benchmark platform for evaluating differential privacy in graph analysis. It includes tunable DP algorithms for graph metrics and a framework to reallocate privacy budget across algorithms, highlighting privacy–accuracy tradeoffs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6100
Venue
SIGMOD
Year
2021
Pagerank
5.6393827e-05
Overall Rank
7,338 | 49.66%
DOI
10.1145/3448016.3452756

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{xia_sigmod21,
        title = {{DPGraph: A Benchmark Platform for Differentially Private Graph Analysis}},
        author = {Xia, Siyuan and Chang, Beizhen and Knopf, Karl and He, Yihan and Tao, Yuchao and He, Xi},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3452756},
        url = {https://dl.acm.org/doi/10.1145/3448016.3452756},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,282 Prism: Private Relational Data Synthesis with Language Models 2026 SIGMOD 5.093636e-05
11,370 Node-Differentially Private Estimation of the Number of Connected Components 2023 PODS 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 12 of 12 cited papers.

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

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