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Minimizing Minimality and Maximizing Utility: Analyzing Method-based attacks on Anonymized Data

Summary: Introduces the minimality (method-based) attack on anonymized data and analyzes its impact across a broad class of privacy methods. Theory and experiments show many algorithms are unaffected or bound the adversary’s belief to small constants; even vulnerable schemes remain utility-preserving, implying practical mitigation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10148
Venue
VLDB
Year
2010
Pagerank
4.3556432e-05
Overall Rank
9,337 | 35.05%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
12,130 Publishing Microdata with a Robust Privacy Guarantee 2012 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

Rank Cited Paper Year Venue Pagerank
455 Incognito: Efficient Full-Domain K-Anonymity 2005 SIGMOD 0.00022717354
654 Anatomy: Simple and Effective Privacy Preservation 2006 VLDB 0.00018613167
1,382 Minimality Attack in Privacy Preserving Data Publishing 2007 VLDB 0.00012281313
2,406 Attacks on Privacy and deFinetti's Theorem 2009 SIGMOD 8.8811954e-05
12,312 Anonymized Data: Generation, Models, Usage 2009 SIGMOD 4.1945683e-05
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