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Privacy-preserving Anonymization of Set-valued Data

Summary: Set-valued data anonymization using a generalization-based k^m-anonymity variant to bound dimensionality; items treated as quasi-identifiers and sensitive data. Optimal algorithm (costly) and two scalable greedy heuristics, evaluated on real datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9663
Venue
VLDB
Year
2008
Pagerank
7.1538038e-05
Overall Rank
3,382 | 76.50%
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
305 On the Complexity of Optimal K-Anonymity 2004 PODS 0.00028264843
458 Incognito: Efficient Full-Domain K-Anonymity 2005 SIGMOD 0.00022698513
654 Anatomy: Simple and Effective Privacy Preservation 2006 VLDB 0.00018594644
2,822 Achieving Anonymity via Clustering 2006 PODS 8.0624025e-05
4,981 Fast Data Anonymization with Low Information Loss 2007 VLDB 5.7823243e-05
6,476 Approximate Algorithms for k-Anonymity 2007 SIGMOD 5.040879e-05
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