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)

Paper ID: h4d40f4b8103071fb
Venue: VLDB
Year: 2009
Pagerank: 6.577479e-05
Overall Rank: 4,454 | 70.15%
DOI: 10.14778/1687627.1687733

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
1,458	PrivBasis: Frequent Itemset Mining with Differential Privacy	2012	VLDB	0.00010562758
2,431	On Differentially Private Frequent Itemset Mining	2013	VLDB	8.4641619e-05
2,925	Publishing Set-Valued Data via Differential Privacy	2011	VLDB	7.8390031e-05
5,634	rho-uncertainty: Inference-Proof Transaction Anonymization	2010	VLDB	6.0387995e-05
8,901	Set-valued Data Publication with Local Privacy: Tight Error Bounds and Efficient Mechanisms	2020	VLDB	5.2398721e-05
9,294	Privacy Preservation by Disassociation	2012	VLDB	5.1860131e-05

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	0.0019892478
29	Fast Algorithms for Mining Association Rules	1994	VLDB	0.00051028332
69	Privacy-Preserving Data Mining	2000	SIGMOD	0.00037815735
406	On the Complexity of Optimal K-Anonymity	2004	PODS	0.00019009895
472	Incognito: Efficient Full-Domain K-Anonymity	2005	SIGMOD	0.00017689274
476	Mining Generalized Association Rules	1995	VLDB	0.00017630424
588	Anatomy: Simple and Effective Privacy Preservation	2006	VLDB	0.00015894454
1,024	m-Invariance: Towards Privacy Preserving Re-publication of Dynamic Datasets	2007	SIGMOD	0.00012396673
1,248	Maintaining Data Privacy in Association Rule Mining	2002	VLDB	0.00011341932
1,714	Injecting Utility into Anonymized Datasets	2006	SIGMOD	9.7943215e-05
3,305	Privacy-preserving Anonymization of Set-valued Data	2008	VLDB	7.4208602e-05
9,198	Dynamic Anonymization: Accurate Statistical Analysis with Privacy Preservation	2008	SIGMOD	5.1954442e-05

Semantically Similar Papers

#	Overall Rank	Paper	Year	Venue
1	472	Incognito: Efficient Full-Domain K-Anonymity	2005	SIGMOD
2	2,925	Publishing Set-Valued Data via Differential Privacy	2011	VLDB
3	9,294	Privacy Preservation by Disassociation	2012	VLDB
4	9,198	Dynamic Anonymization: Accurate Statistical Analysis with Privacy Preservation	2008	SIGMOD
5	12,876	Distribution-based Microdata Anonymization	2009	VLDB
6	3,177	Achieving Anonymity via Clustering	2006	PODS
7	347	Generalizing Data to Provide Anonymity when Disclosing Information	1998	PODS
8	12,845	Anonymized Data: Generation, Models, Usage	2009	SIGMOD
9	4,826	Fast Data Anonymization with Low Information Loss	2007	VLDB
10	3,305	Privacy-preserving Anonymization of Set-valued Data	2008	VLDB