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Frequency Estimation under Local Differential Privacy

Summary: Unifies local differential privacy approaches for frequency estimation and heavy hitter discovery into a single framework, clarifying design tradeoffs. Extensive experiments on millions of users demonstrate that careful algorithm selection yields accurate, scalable frequency estimates for core data-management tasks. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12573
Venue
VLDB
Year
2021
Pagerank
7.7137142e-05
Overall Rank
3,143 | 78.44%
DOI
10.14778/3476249.3476261

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{cormode_vldb21,
        title = {{Frequency Estimation under Local Differential Privacy}},
        author = {Cormode, Graham and Maddock, Samuel and Maple, Carsten},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {11},
        pages = {2046--2058},
        doi = {10.14778/3476249.3476261},
        url = {https://doi.org/10.14778/3476249.3476261},
        year = {2021}
}

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

Rank Cited Paper Year Venue Pagerank
2,542 Privacy at Scale: Local Differential Privacy in Practice 2018 SIGMOD 8.4460386e-05
3,121 Answering Range Queries Under Local Differential Privacy 2019 VLDB 7.7357038e-05
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