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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
3841
Venue
SIGMOD
Year
2007
Pagerank
5.040879e-05
Overall Rank
6,476 | 55.00%
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
182 Mining Frequent Patterns without Candidate Generation 2000 SIGMOD 0.00036955562
225 Generalizing Data to Provide Anonymity when Disclosing Information 1998 PODS 0.0003266103
305 On the Complexity of Optimal K-Anonymity 2004 PODS 0.00028264843
458 Incognito: Efficient Full-Domain K-Anonymity 2005 SIGMOD 0.00022698513
1,733 On k-Anonymity and the Curse of Dimensionality 2005 VLDB 0.00010715774
2,822 Achieving Anonymity via Clustering 2006 PODS 8.0624025e-05
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