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Data Publishing against Realistic Adversaries

Summary: Proposes realistic adversaries—external knowledge with stubbornness—and an epsilon-privacy framework to guard against them. Shows that prior privacy definitions are instantiations of epsilon-privacy, with high utility on real census data amid strong adversaries. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9955
Venue
VLDB
Year
2009
Pagerank
4.5131088e-05
Overall Rank
8,438 | 41.30%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
3,874 Personalized Social Recommendations - Accurate or Private? 2011 VLDB 6.6767405e-05
4,189 Towards an Axiomatization of Statistical Privacy and Utility 2010 PODS 6.3743594e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

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

Rank Cited Paper Year Venue Pagerank
177 Limiting Privacy Breaches in Privacy Preserving Data Mining 2003 PODS 0.0003788711
455 Incognito: Efficient Full-Domain K-Anonymity 2005 SIGMOD 0.00022717354
955 Privacy Preserving OLAP 2005 SIGMOD 0.00015075131
1,083 A Formal Analysis of Information Disclosure in Data Exchange 2004 SIGMOD 0.00014210752
1,761 The Boundary Between Privacy and Utility in Data Publishing 2007 VLDB 0.00010651764
2,682 Personalized Privacy Preservation 2006 SIGMOD 8.3202837e-05
3,760 Output Perturbation with Query Relaxation 2008 VLDB 6.7805033e-05
4,509 Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge 2007 VLDB 6.1270304e-05
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