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
- 2. Tianhao Wang
- 3. Milan Lopuhaä-Zwakenberg
- 4. Ninghui Li
- 5. Boris Škorić
Incoming Citations (Sorted by Pagerank)
Showing 10 of 10 citing papers.
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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 |
|---|---|---|---|---|
| 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 |
| 2,555 | Answering Multi-Dimensional Analytical Queries under Local Differential Privacy | 2019 | SIGMOD | 8.5477878e-05 |
| 3,068 | Answering Range Queries Under Local Differential Privacy | 2019 | SIGMOD | 7.6171639e-05 |
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