Answering Range Queries Under Local Differential Privacy
Summary: Introduces hierarchical-histogram and Haar-wavelet methods for range queries under local differential privacy, where users perturb data before aggregation. Provides variance guarantees and shows wavelets excel at high privacy, while hierarchies win under weaker privacy. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Graham Cormode (University of Warwick)
- 2. Tejas Kulkarni (University of Warwick)
- 3. Divesh Srivastava (AT&T)
BibTeX Citation
@article{cormode_vldb19,
title = {{Answering Range Queries Under Local Differential Privacy}},
author = {Cormode, Graham and Kulkarni, Tejas and Srivastava, Divesh},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {10},
pages = {1126--1138},
doi = {10.14778/3339490.3339496},
url = {https://doi.org/10.14778/3339490.3339496},
year = {2019}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 121 | Boosting the Accuracy of Differentially Private Histograms Through Consistency | 2010 | VLDB | 0.00031639377 |
| 567 | Understanding Hierarchical Methods for Differentially Private Histograms | 2013 | VLDB | 0.00016420715 |
| 1,837 | Marginal Release Under Local Differential Privacy | 2018 | SIGMOD | 9.6443703e-05 |
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