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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
1386
Venue
PODS
Year
2006
Pagerank
7.7477034e-05
Overall Rank
3,110 | 78.67%
DOI
10.1145/1142351.1142374

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{aggarwal_pods06,
        address = {New York, NY, USA},
        series = {{PODS} '06},
        title = {{Achieving Anonymity via Clustering}},
        url = {https://dl.acm.org/doi/10.1145/1142351.1142374},
        doi = {10.1145/1142351.1142374},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Aggarwal, Gagan and Feder, Tomas and Kenthapadi, Krishnaram and Khuller, Samir and Panigrahy, Rina and Thomas, Dilys and Zhu, An},
        year = {2006}
}

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
384 On the Complexity of Optimal K-Anonymity 2004 PODS 0.00019510305
450 Incognito: Efficient Full-Domain K-Anonymity 2005 SIGMOD 0.00018155142
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