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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)

Paper ID
4195
Venue
SIGMOD
Year
2009
Pagerank
4.1945683e-05
Overall Rank
12,312 | 14.35%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
9,337 Minimizing Minimality and Maximizing Utility: Analyzing Method-based attacks on Anonymized Data 2010 VLDB 4.3556432e-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 0.00018613167
803 Towards Identity Anonymization on Graphs 2008 SIGMOD 0.00016478924
1,571 Resisting Structural Re-identification in Anonymized Social Networks 2008 VLDB 0.00011318916
2,560 Foundations of Probabilistic Answers to Queries 2005 SIGMOD 8.5402003e-05
2,718 Anonymizing Bipartite Graph Data using Safe Groupings 2008 VLDB 8.2409647e-05
3,381 Privacy-preserving Anonymization of Set-valued Data 2008 VLDB 7.1604078e-05
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