Optimal Pure Differentially Private Sparse Histograms in Deterministic Linear Time
Summary: Introduces the first deterministic O(n)-time pure-DP sparse histogram algorithm with optimal ℓ∞ error for d≫n, breaking the prior ~O(n²) barrier. A private item-blanket with target-length padding also yields the first near-linear-cost MPC protocol. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Florian Kerschbaum (University of Waterloo)
- 2. Steven Lee (University of Waterloo)
- 3. Hao Wu (University of Waterloo)
BibTeX Citation
@inproceedings{kerschbaum_pods26,
address = {New York, NY, USA},
series = {{PODS} '26},
title = {{Optimal Pure Differentially Private Sparse Histograms in Deterministic Linear Time}},
url = {https://dl.acm.org/doi/10.1145/3801909},
doi = {10.1145/3801909},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Kerschbaum, Florian and Lee, Steven and Wu, Hao},
year = {2026}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,678 | Heavy Hitters and the Structure of Local Privacy | 2018 | PODS | 0.00010032135 |
| 3,854 | Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch | 2023 | PODS | 7.0723123e-05 |
| 7,500 | Approximate DBSCAN under Differential Privacy | 2025 | SIGMOD | 5.6029996e-05 |
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