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)
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Authors
- 1. Nick Koudas
- 2. Divesh Srivastava
- 3. Ting Yu
- 4. Qing Zhang
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Outgoing Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 455 | Incognito: Efficient Full-Domain K-Anonymity | 2005 | SIGMOD | 0.00022717354 |
| 634 | m-Invariance: Towards Privacy Preserving Re-publication of Dynamic Datasets | 2007 | SIGMOD | 0.00018895628 |
| 654 | Anatomy: Simple and Effective Privacy Preservation | 2006 | VLDB | 0.00018613167 |
| 1,571 | Resisting Structural Re-identification in Anonymized Social Networks | 2008 | VLDB | 0.00011318916 |
| 2,718 | Anonymizing Bipartite Graph Data using Safe Groupings | 2008 | VLDB | 8.2409647e-05 |
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