Non-homogeneous Generalization in Privacy Preserving Data Publishing
Summary: Proposes non-homogeneous generalization for k-anonymity, reducing utility loss by varying quasi-identifiers inside partitions. Offers verification, a randomized defense against algorithm-aware attacks, and a partitioning technique that boosts data utility. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. W. K. Wong
- 2. Nikos Mamoulis
- 3. David W. Cheung
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 40 | Privacy-Preserving Data Mining | 2000 | SIGMOD | 0.00074213516 |
| 305 | On the Complexity of Optimal K-Anonymity | 2004 | PODS | 0.00028264843 |
| 458 | Incognito: Efficient Full-Domain K-Anonymity | 2005 | SIGMOD | 0.00022698513 |
| 633 | m-Invariance: Towards Privacy Preserving Re-publication of Dynamic Datasets | 2007 | SIGMOD | 0.00018881387 |
| 654 | Anatomy: Simple and Effective Privacy Preservation | 2006 | VLDB | 0.00018594644 |
| 1,382 | Minimality Attack in Privacy Preserving Data Publishing | 2007 | VLDB | 0.00012270633 |
| 1,582 | The New Casper: Query Processing for Location Services without Compromising Privacy | 2006 | VLDB | 0.00011262294 |
| 2,404 | Attacks on Privacy and deFinetti's Theorem | 2009 | SIGMOD | 8.8736984e-05 |
| 4,905 | Deriving Private Information from Randomized Data | 2005 | SIGMOD | 5.8382391e-05 |
| 4,981 | Fast Data Anonymization with Low Information Loss | 2007 | VLDB | 5.7823243e-05 |
| 7,772 | K-Anonymization as Spatial Indexing: Toward Scalable and Incremental Anonymization | 2007 | VLDB | 4.6509661e-05 |
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