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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)

Paper ID
2006
Venue
PODS
Year
2025
Pagerank
5.2528121e-05
Overall Rank
9,538 | 34.57%
DOI
10.1145/3725240

Incoming Non-self Citations Over Time

Authors

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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