On the Complexity of Optimal K-Anonymity
Summary: Proves NP-hardness of two general formulations of optimal k-anonymity, including suppression (minimize deleted entries). Gives poly-time approximation algorithms: an O(k log k)-approx for constant k (runtime exponential in k) and an O(k log m)-approx removing that dependence on k. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Adam Meyerson (Carnegie Mellon University; University of California Los Angeles)
- 2. Ryan Williams (Carnegie Mellon University)
BibTeX Citation
@inproceedings{meyerson_pods04,
address = {New York, NY, USA},
series = {{PODS} '04},
title = {{On the Complexity of Optimal K-Anonymity}},
url = {https://dl.acm.org/doi/10.1145/1055558.1055591},
doi = {10.1145/1055558.1055591},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Meyerson, Adam and Williams, Ryan},
year = {2004}
}
Incoming Citations (Sorted by Pagerank)
Showing 27 of 27 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 68 | Privacy-Preserving Data Mining | 2000 | SIGMOD | 0.00037958605 |
| 122 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.00030770793 |
| 226 | Limiting Privacy Breaches in Privacy Preserving Data Mining | 2003 | PODS | 0.00023984903 |
| 254 | On the Design and Quantification of Privacy Preserving Data Mining Algorithms | 2001 | PODS | 0.00023020039 |
| 347 | Generalizing Data to Provide Anonymity when Disclosing Information | 1998 | PODS | 0.00020325683 |
| 411 | Hippocratic Databases | 2002 | VLDB | 0.00018699914 |
| 2,050 | Auditing Boolean Attributes | 2000 | PODS | 9.1201864e-05 |
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