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Federated Heavy Hitter Analytics with Local Differential Privacy

Summary: Introduces a target-aligning prefix-tree mechanism with epsilon-LDP for federated heavy hitter analytics. Adaptive extension balances prefix coverage with local heavy hitter estimation; consensus pruning uses cross-party priors to reveal hitters. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7140
Venue
SIGMOD
Year
2025
Pagerank
6.1017514e-05
Overall Rank
5,746 | 60.58%
DOI
10.1145/3709739

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zhang_sigmod25,
        title = {{Federated Heavy Hitter Analytics with Local Differential Privacy}},
        author = {Zhang, Yuemin and Ye, Qingqing and Hu, Haibo},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3709739},
        url = {https://dl.acm.org/doi/10.1145/3709739},
        year = {2025}
}

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