PriPL-Tree: Accurate Range Query for Arbitrary Distribution under Local Differential Privacy
Summary: PriPL-Tree: hierarchical LDP structure that models node distributions with piecewise-linear fits instead of the usual uniform-within-partition assumption, improving range-query accuracy with few segments. Multidimensional extension uses data-aware adaptive grids built from PriPL marginals. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Leixia Wang (Renmin University of China)
- 2. Qingqing Ye (Hong Kong Polytechnic University)
- 3. Haibo Hu (Hong Kong Polytechnic University)
- 4. Xiaofeng Meng (Renmin University of China)
BibTeX Citation
@article{wang_vldb24,
title = {{PriPL-Tree: Accurate Range Query for Arbitrary Distribution under Local Differential Privacy}},
author = {Wang, Leixia and Ye, Qingqing and Hu, Haibo and Meng, Xiaofeng},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {3031--3044},
doi = {10.14778/3681954.3681981},
url = {https://doi.org/10.14778/3681954.3681981},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,427 | Femur: A Flexible Framework for Fast and Secure Querying from Public Key-Value Store | 2025 | SIGMOD | 5.3324907e-05 |
| 10,586 | High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff | 2026 | SIGMOD | 4.9769913e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 18 of 18 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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