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Acyclic Graph Pattern Counting under Local Differential Privacy

Summary: First general LDP framework for counting arbitrary acyclic graph patterns beyond triangles and stars. Recursive subpattern construction and random node marking prevent duplication, achieving Õ(√(N d(G)^k)) error with substantial utility and communication gains. (summarized by gpt-5.6-luna on Jul 26 2026)

Paper ID
7379
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,188 | 30.11%
DOI
10.1145/3802006

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BibTeX Citation

@inproceedings{hu_sigmod26,
        title = {{Acyclic Graph Pattern Counting under Local Differential Privacy}},
        author = {Hu, Yihua and Wang, Kuncan and Dong, Wei},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3802006},
        url = {https://dl.acm.org/doi/10.1145/3802006},
        year = {2026}
}

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