On k-Anonymity and the Curse of Dimensionality

Summary: Analyzes k-anonymity in high-dimensional data by modeling inference attacks over all attribute combinations. Demonstrates dimensionality-driven sparsity and an exponential attack space, forcing either heavy data suppression or privacy compromise. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 9558
Venue: VLDB
Year: 2005
Pagerank: 9.6832973e-05
Overall Rank: 1,817 | 87.54%
DOI: -

Incoming Non-self Citations Over Time

Authors

1. Charu C. Aggarwal (IBM)

BibTeX Citation

@article{aggarwal_vldb05,
        title = {{On k-Anonymity and the Curse of Dimensionality}},
        author = {Aggarwal, Charu C.},
        journal = {PVLDB},
        series = {{VLDB} '05},
        volume = {31},
        pages = {901--909},
        year = {2005}
}

Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
572	Anatomy: Simple and Effective Privacy Preservation	2006	VLDB	0.00016316092
1,010	m-Invariance: Towards Privacy Preserving Re-publication of Dynamic Datasets	2007	SIGMOD	0.00012684067
1,421	PrivBasis: Frequent Itemset Mining with Differential Privacy	2012	VLDB	0.00010828328
1,669	Injecting Utility into Anonymized Datasets	2006	SIGMOD	0.00010050522
2,397	Personalized Privacy Preservation	2006	SIGMOD	8.6343421e-05
4,526	Hiding the Presence of Individuals from Shared Databases	2007	SIGMOD	6.6452861e-05
4,629	Time Series Compressibility and Privacy	2007	VLDB	6.5960717e-05
6,554	Approximate Algorithms for k-Anonymity	2007	SIGMOD	5.8416386e-05
9,001	Dynamic Anonymization: Accurate Statistical Analysis with Privacy Preservation	2008	SIGMOD	5.334788e-05
9,086	Privacy Preservation by Disassociation	2012	VLDB	5.3251649e-05

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
70	Privacy-Preserving Data Mining	2000	SIGMOD	0.0003804755
244	On the Design and Quantification of Privacy Preserving Data Mining Algorithms	2001	PODS	0.00023476901
384	On the Complexity of Optimal K-Anonymity	2004	PODS	0.00019510305

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