Distribution-based Microdata Anonymization

Summary: Proposes distribution-based microdata anonymization for privacy models (e.g., t-closeness) with target distributions over sensitive values. Combines permutation, generalization, and fake-value insertion to meet those distributions with minimal distortion, optimizing aggregate-accuracy metrics. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 10139
Venue: VLDB
Year: 2009
Pagerank: 5.093636e-05
Overall Rank: 12,537 | 13.99%
DOI: 10.14778/1687627.1687735

Incoming Non-self Citations Over Time

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Authors

1. Nick Koudas (University of Toronto)
2. Divesh Srivastava (AT&T)
3. Ting Yu (North Carolina State University)
4. Qing Zhang (Teradata)

BibTeX Citation

@article{koudas_vldb09,
        title = {{Distribution-based Microdata Anonymization}},
        author = {Koudas, Nick and Srivastava, Divesh and Yu, Ting and Zhang, Qing},
        journal = {PVLDB},
        series = {{VLDB} '09},
        doi = {10.14778/1687627.1687735},
        url = {https://doi.org/10.14778/1687627.1687735},
        year = {2009}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
450	Incognito: Efficient Full-Domain K-Anonymity	2005	SIGMOD	0.00018155142
572	Anatomy: Simple and Effective Privacy Preservation	2006	VLDB	0.00016316092
1,010	m-Invariance: Towards Privacy Preserving Re-publication of Dynamic Datasets	2007	SIGMOD	0.00012684067
1,326	Resisting Structural Re-identification in Anonymized Social Networks	2008	VLDB	0.00011145219
2,430	Anonymizing Bipartite Graph Data using Safe Groupings	2008	VLDB	8.5885381e-05

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