PrivRM: A Framework for Range Mean Estimation under Local Differential Privacy
Summary: PrivRM provides a framework for private range-mean estimation under local differential privacy. It yields two adaptable implementations (PrivRMI, PrivRM*) adaptable to numerical perturbation mechanisms and a distribution-aware AA strategy to tighten perturbation on skewed data, achieving notable accuracy gains over prior LDP methods under identical privacy budgets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Liantong Yu (Hong Kong Polytechnic University)
- 2. Qingqing Ye (Hong Kong Polytechnic University)
- 3. Rong Du (Hong Kong Polytechnic University)
BibTeX Citation
@inproceedings{yu_sigmod25,
title = {{PrivRM: A Framework for Range Mean Estimation under Local Differential Privacy}},
author = {Yu, Liantong and Ye, Qingqing and Du, Rong},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725414},
url = {https://dl.acm.org/doi/10.1145/3725414},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,442 | Defense against Poisoning Attacks under Shuffle-DP | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 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 |
| 1,678 | Heavy Hitters and the Structure of Local Privacy | 2018 | PODS | 0.00010032135 |
| 1,837 | Marginal Release Under Local Differential Privacy | 2018 | SIGMOD | 9.6443703e-05 |
| 2,279 | Answering Multi-Dimensional Analytical Queries under Local Differential Privacy | 2019 | SIGMOD | 8.8155688e-05 |
| 2,542 | Privacy at Scale: Local Differential Privacy in Practice | 2018 | SIGMOD | 8.4460386e-05 |
| 2,749 | Estimating Numerical Distributions under Local Differential Privacy | 2020 | SIGMOD | 8.1688076e-05 |
| 3,113 | CGM: An Enhanced Mechanism for Streaming Data Collection with Local Differential Privacy | 2021 | VLDB | 7.7415187e-05 |
| 3,260 | Answering Range Queries Under Local Differential Privacy | 2019 | SIGMOD | 7.589057e-05 |
| 5,763 | LDPTrace: Locally Differentially Private Trajectory Synthesis | 2023 | VLDB | 6.0955612e-05 |
| 11,444 | Trajectory Data Collection with Local Differential Privacy | 2023 | VLDB | 5.093636e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 3,260 | Answering Range Queries Under Local Differential Privacy | 2019 | SIGMOD |
| 2 | 3,143 | Frequency Estimation under Local Differential Privacy | 2021 | VLDB |
| 3 | 11,369 | Universal Private Estimators | 2023 | PODS |
| 4 | 10,451 | Enhancing Local Differential Privacy Accuracy by Exploiting Inherent Uncertainty | 2026 | SIGMOD |
| 5 | 2,749 | Estimating Numerical Distributions under Local Differential Privacy | 2020 | SIGMOD |
| 6 | 3,121 | Answering Range Queries Under Local Differential Privacy | 2019 | VLDB |
| 7 | 10,335 | A General Framework for Per-record Differential Privacy | 2026 | SIGMOD |
| 8 | 1,956 | Low-Rank Mechanism: Optimizing Batch Queries under Differential Privacy | 2012 | VLDB |
| 9 | 11,230 | AAA: an Adaptive Mechanism for Locally Differentially Private Mean Estimation | 2024 | VLDB |
| 10 | 10,838 | Privacy for Free: Leveraging Local Differential Privacy Perturbed Data from Multiple Services | 2025 | VLDB |