Optimal Bounds for Private Minimum Spanning Trees via Input Perturbation
Summary: Input perturbation enables reuse of a non-private MST algorithm to yield a private MST under DP; experiments validate. Link to Top-k Selection yields privacy-utility lower bound for MST under approximate DP; O~(n^{3/2}) error is optimal up to logs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Rasmus Pagh (University of Copenhagen)
- 2. Lukas Retschmeier (University of Copenhagen)
- 3. Hao Wu (University of Waterloo)
- 4. Hanwen Zhang (University of Copenhagen)
BibTeX Citation
@inproceedings{pagh_pods25,
address = {New York, NY, USA},
series = {{PODS} '25},
title = {{Optimal Bounds for Private Minimum Spanning Trees via Input Perturbation}},
url = {https://dl.acm.org/doi/10.1145/3725240},
doi = {10.1145/3725240},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Pagh, Rasmus and Retschmeier, Lukas and Wu, Hao and Zhang, Hanwen},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,158 | Improved Lower Bounds for Privacy under Continual Release | 2026 | PODS | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,948 | Shortest Paths and Distances with Differential Privacy | 2016 | PODS | 6.9993183e-05 |
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