DBScholar

Back to papers

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)

Paper ID
5977
Venue
SIGMOD
Year
2020
Pagerank
8.1688076e-05
Overall Rank
2,749 | 81.15%
DOI
10.1145/3318464.3389700

Incoming Non-self Citations Over Time

Authors

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)

Showing 10 of 10 citing papers.

Previous Page 1 / 1 Next

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.

Previous Page 1 / 1 Next

Semantically Similar Papers