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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
12385
Venue
VLDB
Year
2021
Pagerank
8.5797299e-05
Overall Rank
2,540 | 82.34%
DOI
10.14778/3476249.3476261

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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
2,899 Privacy at Scale: Local Differential Privacy in Practice 2018 SIGMOD 7.9443198e-05
3,399 Answering Range Queries Under Local Differential Privacy 2019 VLDB 7.1408089e-05
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