K-Anonymization as Spatial Indexing: Toward Scalable and Incremental Anonymization
Summary: K-anonymization reframed as spatial indexing with R-trees, enabling scalable, incremental anonymization. Batch anonymization with R-trees delivers orders-of-magnitude speedups and yields superior quality by standard metrics through effective partitioning. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tochukwu Iwuchukwu (University of Wisconsin)
- 2. Jeffrey F. Naughton (University of Wisconsin)
BibTeX Citation
@article{iwuchukwu_vldb07,
title = {{K-Anonymization as Spatial Indexing: Toward Scalable and Incremental Anonymization}},
author = {Iwuchukwu, Tochukwu and Naughton, Jeffrey F.},
journal = {PVLDB},
series = {{VLDB} '07},
pages = {746--757},
year = {2007}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,001 | Dynamic Anonymization: Accurate Statistical Analysis with Privacy Preservation | 2008 | SIGMOD | 5.334788e-05 |
| 9,487 | Preservation of Proximity Privacy in Publishing Numerical Sensitive Data | 2008 | SIGMOD | 5.2634238e-05 |
| 12,424 | Non-homogeneous Generalization in Privacy Preserving Data Publishing | 2010 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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