Anonymization of Set-Valued Data via Top-Down, Local Generalization
Summary: Top-down, partition-based anonymization for set-valued data, addressing multi-valued records where single-value models fail. Linear-time scalability and a strong information-loss metric; enables practical anonymization of AOL query logs and related data releases. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yeye He (University of Wisconsin)
- 2. Jeffrey F. Naughton (University of Wisconsin)
BibTeX Citation
@article{he_vldb09,
title = {{Anonymization of Set-Valued Data via Top-Down, Local Generalization}},
author = {He, Yeye and Naughton, Jeffrey F.},
journal = {PVLDB},
series = {{VLDB} '09},
doi = {10.14778/1687627.1687733},
url = {https://doi.org/10.14778/1687627.1687733},
year = {2009}
}
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,453 | PrivBasis: Frequent Itemset Mining with Differential Privacy | 2012 | VLDB | 0.0001060277 |
| 2,416 | On Differentially Private Frequent Itemset Mining | 2013 | VLDB | 8.4962257e-05 |
| 2,912 | Publishing Set-Valued Data via Differential Privacy | 2011 | VLDB | 7.8687198e-05 |
| 5,620 | rho-uncertainty: Inference-Proof Transaction Anonymization | 2010 | VLDB | 6.0617004e-05 |
| 8,871 | Set-valued Data Publication with Local Privacy: Tight Error Bounds and Efficient Mechanisms | 2020 | VLDB | 5.2597458e-05 |
| 9,260 | Privacy Preservation by Disassociation | 2012 | VLDB | 5.2056825e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2 | R-Trees: A Dynamic Index Structure For Spatial Searching | 1984 | SIGMOD | 0.001992968 |
| 29 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.0005121339 |
| 68 | Privacy-Preserving Data Mining | 2000 | SIGMOD | 0.00037958605 |
| 404 | On the Complexity of Optimal K-Anonymity | 2004 | PODS | 0.00019081867 |
| 469 | Incognito: Efficient Full-Domain K-Anonymity | 2005 | SIGMOD | 0.0001775622 |
| 475 | Mining Generalized Association Rules | 1995 | VLDB | 0.00017672519 |
| 584 | Anatomy: Simple and Effective Privacy Preservation | 2006 | VLDB | 0.00015954725 |
| 1,020 | m-Invariance: Towards Privacy Preserving Re-publication of Dynamic Datasets | 2007 | SIGMOD | 0.00012443542 |
| 1,239 | Maintaining Data Privacy in Association Rule Mining | 2002 | VLDB | 0.00011384824 |
| 1,705 | Injecting Utility into Anonymized Datasets | 2006 | SIGMOD | 9.831451e-05 |
| 3,293 | Privacy-preserving Anonymization of Set-valued Data | 2008 | VLDB | 7.4490004e-05 |
| 9,164 | Dynamic Anonymization: Accurate Statistical Analysis with Privacy Preservation | 2008 | SIGMOD | 5.2151493e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 469 | Incognito: Efficient Full-Domain K-Anonymity | 2005 | SIGMOD |
| 2 | 2,912 | Publishing Set-Valued Data via Differential Privacy | 2011 | VLDB |
| 3 | 9,260 | Privacy Preservation by Disassociation | 2012 | VLDB |
| 4 | 9,164 | Dynamic Anonymization: Accurate Statistical Analysis with Privacy Preservation | 2008 | SIGMOD |
| 5 | 12,827 | Distribution-based Microdata Anonymization | 2009 | VLDB |
| 6 | 3,165 | Achieving Anonymity via Clustering | 2006 | PODS |
| 7 | 347 | Generalizing Data to Provide Anonymity when Disclosing Information | 1998 | PODS |
| 8 | 12,796 | Anonymized Data: Generation, Models, Usage | 2009 | SIGMOD |
| 9 | 4,802 | Fast Data Anonymization with Low Information Loss | 2007 | VLDB |
| 10 | 3,293 | Privacy-preserving Anonymization of Set-valued Data | 2008 | VLDB |