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Privacy-preserving Anonymization of Set-valued Data

Summary: Introduces k^m-anonymity for set-valued transactions, treating every item as potentially identifying or sensitive under partial-set knowledge. Uses generalization rather than suppression, with an optimal algorithm and scalable greedy heuristics for near-optimal anonymization. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
heb3cfa9aea516371
Venue
VLDB
Year
2008
Pagerank
7.4490004e-05
Overall Rank
3,293 | 77.87%
DOI
10.14778/1453856.1453874

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{terrovitis_vldb08,
        title = {{Privacy-preserving Anonymization of Set-valued Data}},
        author = {Terrovitis, Manolis and Mamoulis, Nikos and Kalnis, Panos},
        journal = {PVLDB},
        series = {{VLDB} '08},
        pages = {115},
        doi = {10.14778/1453856.1453874},
        url = {https://doi.org/10.14778/1453856.1453874},
        year = {2008}
}

Incoming Citations (Sorted by Pagerank)

Showing 8 of 8 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

Rank Cited Paper Year Venue Pagerank
164 Mining Frequent Patterns without Candidate Generation 2000 SIGMOD 0.00027412227
404 On the Complexity of Optimal K-Anonymity 2004 PODS 0.00019081867
469 Incognito: Efficient Full-Domain K-Anonymity 2005 SIGMOD 0.0001775622
584 Anatomy: Simple and Effective Privacy Preservation 2006 VLDB 0.00015954725
3,165 Achieving Anonymity via Clustering 2006 PODS 7.5767906e-05
4,802 Fast Data Anonymization with Low Information Loss 2007 VLDB 6.4095862e-05
6,678 Approximate Algorithms for k-Anonymity 2007 SIGMOD 5.710693e-05
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