Estimating Numerical Distributions under Local Differential Privacy
Summary: Numerical-domain local differential privacy for distribution estimation using a square wave (SW) reporting mechanism that exploits domain structure to improve privacy-utility over discretization. An EMS (Expectation Maximization with Smoothing) algorithm on SW histograms recovers the original distribution, with experiments showing SW+EMS consistently outperforms baselines on utility metrics. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zitao Li (Purdue University)
- 2. Tianhao Wang (Purdue University)
- 3. Milan Lopuhaä-Zwakenberg (Eindhoven University of Technology)
- 4. Ninghui Li (Purdue University)
- 5. Boris Škorić (Eindhoven University of Technology)
BibTeX Citation
@inproceedings{li_sigmod20,
title = {{Estimating Numerical Distributions under Local Differential Privacy}},
author = {Li, Zitao and Wang, Tianhao and Lopuhaä-Zwakenberg, Milan and Li, Ninghui and Škorić, Boris},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389700},
url = {https://dl.acm.org/doi/10.1145/3318464.3389700},
year = {2020}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 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 |
| 2,279 | Answering Multi-Dimensional Analytical Queries under Local Differential Privacy | 2019 | SIGMOD | 8.8155688e-05 |
| 3,260 | Answering Range Queries Under Local Differential Privacy | 2019 | SIGMOD | 7.589057e-05 |
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