Anonymized Data: Generation, Models, Usage
Summary: Unified framework for data anonymization via uncertainty; anonymized data are possible worlds from suppression, generalization, perturbation, or permutation. Links k-anonymity and l-diversity to the possible-world view and applies uncertain-database query evaluation for ad-hoc queries, highlighting new directions. (summarized by gpt-5-nano on Feb 09 2026)
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Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,342 | Minimizing Minimality and Maximizing Utility: Analyzing Method-based attacks on Anonymized Data | 2010 | VLDB | 4.351469e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 654 | Anatomy: Simple and Effective Privacy Preservation | 2006 | VLDB | 0.00018594644 |
| 800 | Towards Identity Anonymization on Graphs | 2008 | SIGMOD | 0.00016463554 |
| 1,571 | Resisting Structural Re-identification in Anonymized Social Networks | 2008 | VLDB | 0.000113084 |
| 2,564 | Foundations of Probabilistic Answers to Queries | 2005 | SIGMOD | 8.5325119e-05 |
| 2,721 | Anonymizing Bipartite Graph Data using Safe Groupings | 2008 | VLDB | 8.2331032e-05 |
| 3,382 | Privacy-preserving Anonymization of Set-valued Data | 2008 | VLDB | 7.1538038e-05 |
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