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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
- 12561
- Venue
- VLDB
- Year
- 2021
- Pagerank
- 7.1718964e-05
- Overall Rank
- 3,361 | 76.65%
- 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 |
0.00037726596 |
| 714 |
Understanding Hierarchical Methods for Differentially Private Histograms |
2013 |
VLDB |
0.00017670757 |
| 742 |
Optimizing Linear Counting Queries Under Differential Privacy |
2010 |
PODS |
0.00017336928 |
| 1,762 |
PriView: Practical Differentially Private Release of Marginal Contingency Tables |
2014 |
SIGMOD |
0.00010629368 |
| 1,928 |
A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy |
2014 |
VLDB |
0.00010062105 |
| 1,935 |
Marginal Release Under Local Differential Privacy |
2018 |
SIGMOD |
0.00010033955 |
| 2,408 |
Estimating Numerical Distributions under Local Differential Privacy |
2020 |
SIGMOD |
8.8694564e-05 |
| 2,436 |
Optimizing error of high-dimensional statistical queries under differential privacy |
2018 |
VLDB |
8.8217855e-05 |
| 2,461 |
Principled Evaluation of Differentially Private Algorithms using DPBench |
2016 |
SIGMOD |
8.7486072e-05 |
| 2,556 |
Answering Multi-Dimensional Analytical Queries under Local Differential Privacy |
2019 |
SIGMOD |
8.5485513e-05 |
| 3,401 |
Answering Range Queries Under Local Differential Privacy |
2019 |
VLDB |
7.1343786e-05 |
| 7,471 |
A workload-adaptive mechanism for linear queries under local differential privacy |
2020 |
VLDB |
4.7158603e-05 |
| 8,077 |
Set-valued Data Publication with Local Privacy: Tight Error Bounds and Efficient Mechanisms |
2020 |
VLDB |
4.5878947e-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 |
4.3161588e-05 |
| 2,061 |
Low-Rank Mechanism: Optimizing Batch Queries under Differential Privacy |
2012 |
VLDB |
9.6578455e-05 |
| 9,290 |
PriPL-Tree: Accurate Range Query for Arbitrary Distribution under Local Differential Privacy |
2024 |
VLDB |
4.358174e-05 |
| 7,034 |
A Neural Database for Differentially Private Spatial Range Queries |
2022 |
VLDB |
4.8504321e-05 |
| 11,229 |
On the Risks of Collecting Multidimensional Data Under Local Differential Privacy |
2023 |
VLDB |
4.1905499e-05 |
| 2,436 |
Optimizing error of high-dimensional statistical queries under differential privacy |
2018 |
VLDB |
8.8217855e-05 |
| 1,928 |
A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy |
2014 |
VLDB |
0.00010062105 |
| 3,401 |
Answering Range Queries Under Local Differential Privacy |
2019 |
VLDB |
7.1343786e-05 |
| 3,073 |
Answering Range Queries Under Local Differential Privacy |
2019 |
SIGMOD |
7.6098046e-05 |
| 2,556 |
Answering Multi-Dimensional Analytical Queries under Local Differential Privacy |
2019 |
SIGMOD |
8.5485513e-05 |