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Approximate Algorithms for k-Anonymity

Summary: Proposes approximation algorithms for k-anonymity in data publishing, addressing linking attacks on quasi-identifiers. Delivers O(log k)-approximation guarantees and O(beta log k)-time variants, outperforming O(k) and O(k log k) baselines; experiments show practical gains. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3840
Venue
SIGMOD
Year
2007
Pagerank
5.045711e-05
Overall Rank
6,482 | 54.91%
DOI
-

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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
181 Mining Frequent Patterns without Candidate Generation 2000 SIGMOD 0.00036992674
225 Generalizing Data to Provide Anonymity when Disclosing Information 1998 PODS 0.00032707646
304 On the Complexity of Optimal K-Anonymity 2004 PODS 0.00028290121
455 Incognito: Efficient Full-Domain K-Anonymity 2005 SIGMOD 0.00022717354
1,735 On k-Anonymity and the Curse of Dimensionality 2005 VLDB 0.00010723402
2,815 Achieving Anonymity via Clustering 2006 PODS 8.0702535e-05
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