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Answering Multi-Dimensional Range Queries under Local Differential Privacy
Summary: Local differential privacy for multi-dimensional range queries; introduces Two-Dimensional Grids (TDG) that partition 2-D attribute domains into grids to answer all 2-D ranges and extrapolate to higher dimensions. To overcome loss of fine-grained information, Hybrid-Dimensional Grids (HDG) combines 1-D and 2-D grids with a principled granularity guideline, yielding substantial accuracy gains over prior approaches on real and synthetic data.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 12560
- Venue
- VLDB
- Year
- 2021
- Pagerank
- 7.1714763e-05
- Overall Rank
- 3,368 | 76.58%
- DOI
-
10.14778/3430915.3430927
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 178 |
Boosting the Accuracy of Differentially Private Histograms Through Consistency |
2010 |
VLDB |
0.00037697111 |
| 719 |
Understanding Hierarchical Methods for Differentially Private Histograms |
2013 |
VLDB |
0.00017626484 |
| 742 |
Optimizing Linear Counting Queries Under Differential Privacy |
2010 |
PODS |
0.00017360873 |
| 1,764 |
PriView: Practical Differentially Private Release of Marginal Contingency Tables |
2014 |
SIGMOD |
0.00010636626 |
| 1,930 |
Marginal Release Under Local Differential Privacy |
2018 |
SIGMOD |
0.00010040732 |
| 1,935 |
A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy |
2014 |
VLDB |
0.00010032967 |
| 2,408 |
Estimating Numerical Distributions under Local Differential Privacy |
2020 |
SIGMOD |
8.8780076e-05 |
| 2,434 |
Optimizing error of high-dimensional statistical queries under differential privacy |
2018 |
VLDB |
8.8278955e-05 |
| 2,465 |
Principled Evaluation of Differentially Private Algorithms using DPBench |
2016 |
SIGMOD |
8.7518123e-05 |
| 2,555 |
Answering Multi-Dimensional Analytical Queries under Local Differential Privacy |
2019 |
SIGMOD |
8.5477878e-05 |
| 3,399 |
Answering Range Queries Under Local Differential Privacy |
2019 |
VLDB |
7.1408089e-05 |
| 7,471 |
A workload-adaptive mechanism for linear queries under local differential privacy |
2020 |
VLDB |
4.7199888e-05 |
| 8,074 |
Set-valued Data Publication with Local Privacy: Tight Error Bounds and Efficient Mechanisms |
2020 |
VLDB |
4.5918992e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 9,592 |
HDPView: Differentially Private Materialized View for Exploring High Dimensional Relational Data |
2022 |
VLDB |
4.3202988e-05 |
| 2,052 |
Low-Rank Mechanism: Optimizing Batch Queries under Differential Privacy |
2012 |
VLDB |
9.676612e-05 |
| 9,285 |
PriPL-Tree: Accurate Range Query for Arbitrary Distribution under Local Differential Privacy |
2024 |
VLDB |
4.3623546e-05 |
| 7,034 |
A Neural Database for Differentially Private Spatial Range Queries |
2022 |
VLDB |
4.8550912e-05 |
| 11,227 |
On the Risks of Collecting Multidimensional Data Under Local Differential Privacy |
2023 |
VLDB |
4.1945683e-05 |
| 2,434 |
Optimizing error of high-dimensional statistical queries under differential privacy |
2018 |
VLDB |
8.8278955e-05 |
| 1,935 |
A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy |
2014 |
VLDB |
0.00010032967 |
| 3,399 |
Answering Range Queries Under Local Differential Privacy |
2019 |
VLDB |
7.1408089e-05 |
| 3,068 |
Answering Range Queries Under Local Differential Privacy |
2019 |
SIGMOD |
7.6171639e-05 |
| 2,555 |
Answering Multi-Dimensional Analytical Queries under Local Differential Privacy |
2019 |
SIGMOD |
8.5477878e-05 |