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Differentially Private Hierarchical Heavy Hitters

Summary: Formalizes differentially private hierarchical heavy hitters (DP-HHH) for both streaming and non-streaming data. Non-streaming: relative error for any prefix is independent of hierarchy height and the number of heavy hitters; streaming: absolute error is space-independent despite high sensitivity of streaming approximations, improving Ghazi et al.'s tree-counting bounds. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
1976
Venue
PODS
Year
2024
Pagerank
5.5357919e-05
Overall Rank
7,831 | 46.28%
DOI
10.1145/3695826

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{biswas_pods24,
        address = {New York, NY, USA},
        series = {{PODS} '24},
        title = {{Differentially Private Hierarchical Heavy Hitters}},
        url = {https://dl.acm.org/doi/10.1145/3695826},
        doi = {10.1145/3695826},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Biswas, Ari and Cormode, Graham and Kanza, Yaron and Srivastava, Divesh and Zhou, Zhengyi},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,158 Improved Lower Bounds for Privacy under Continual Release 2026 PODS 5.093636e-05
10,647 Private Synthetic Data Generation in Bounded Memory 2025 PODS 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

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

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