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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
9367
Venue
VLDB
Year
2005
Pagerank
0.00010723402
Overall Rank
1,735 | 87.94%
DOI
-

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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 0.00074232718
147 On the Design and Quantification of Privacy Preserving Data Mining Algorithms 2001 PODS 0.00041235556
304 On the Complexity of Optimal K-Anonymity 2004 PODS 0.00028290121
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