Database Paper Browser

Back to papers

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
9948
Venue
VLDB
Year
2009
Pagerank
4.1945683e-05
Overall Rank
12,343 | 14.14%
DOI
-

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank Citing Paper Year Venue Pagerank
Previous Page 1 / 1 Next

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.

Previous Page 1 / 1 Next

Semantically Similar Papers

Overall Rank Paper Year Venue Pagerank
8,930 Privacy Preservation by Disassociation 2012 VLDB 4.427232e-05
225 Generalizing Data to Provide Anonymity when Disclosing Information 1998 PODS 0.00032707646
4,979 Fast Data Anonymization with Low Information Loss 2007 VLDB 5.7878768e-05
12,229 Non-homogeneous Generalization in Privacy Preserving Data Publishing 2010 SIGMOD 4.1945683e-05
2,682 Personalized Privacy Preservation 2006 SIGMOD 8.3202837e-05
3,381 Privacy-preserving Anonymization of Set-valued Data 2008 VLDB 7.1604078e-05
2,815 Achieving Anonymity via Clustering 2006 PODS 8.0702535e-05
12,130 Publishing Microdata with a Robust Privacy Guarantee 2012 VLDB 4.1945683e-05
12,312 Anonymized Data: Generation, Models, Usage 2009 SIGMOD 4.1945683e-05
8,794 Dynamic Anonymization: Accurate Statistical Analysis with Privacy Preservation 2008 SIGMOD 4.4502028e-05