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Achieving Anonymity via Clustering

Summary: Propose clustering-based anonymization: publish cluster centers with each cluster containing ≥k records, offering richer generalization and lower distortion than k-anonymity. Provide constant-factor approximation algorithms independent of k and an ε-outlier deletion variant. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1387
Venue
PODS
Year
2006
Pagerank
8.0702535e-05
Overall Rank
2,815 | 80.42%
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
304 On the Complexity of Optimal K-Anonymity 2004 PODS 0.00028290121
455 Incognito: Efficient Full-Domain K-Anonymity 2005 SIGMOD 0.00022717354
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