Efficient and Effective Biclique Counting with Local Differential Privacy
Summary: Edge-LDP (p,q)-biclique counting in bipartite graphs. Key novelty: moment-based Multi-round Common Neighbor estimator with variance reduction and pruning, yielding unbiased counts without explicit motif enumeration; substantially better utility/scalability than naive randomized-response. (summarized by gpt-5.4-mini on Apr 11 2026)
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Authors
- 1. Yizhang He (University of New South Wales)
- 2. Wenjie Zhang (University of New South Wales)
- 3. Kai Wang (Shanghai Jiao Tong University)
- 4. Xuemin Lin (Shanghai Jiao Tong University)
- 5. Ying Zhang (University of Technology Sydney)
- 6. Wei Ni (University of New South Wales)
BibTeX Citation
@inproceedings{he_sigmod26,
title = {{Efficient and Effective Biclique Counting with Local Differential Privacy}},
author = {He, Yizhang and Zhang, Wenjie and Wang, Kai and Lin, Xuemin and Zhang, Ying and Ni, Wei},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786642},
url = {https://dl.acm.org/doi/10.1145/3786642},
year = {2026}
}
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