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)
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Authors
- 1. Yihua Hu (Nanyang Technological University)
- 2. Kuncan Wang (Nanyang Technological University)
- 3. Wei Dong (Nanyang Technological University)
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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