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Deriving Private Information from Randomized Data

Summary: Correlations drive leakage in randomized data release; PCA and Bayes reconstructions quantify disclosure risk. Proposes correlated-noise randomization; increasing data-noise similarity reduces reconstruction accuracy; experiments validate privacy gains. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3622
Venue
SIGMOD
Year
2005
Pagerank
5.8439867e-05
Overall Rank
4,899 | 65.92%
DOI
-

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Showing 4 of 4 cited papers.

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
177 Limiting Privacy Breaches in Privacy Preserving Data Mining 2003 PODS 0.0003788711
559 Maintaining Data Privacy in Association Rule Mining 2002 VLDB 0.00020147576
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4,794 Optimal Random Perturbation at Multiple Privacy Levels 2009 VLDB 5.9161511e-05
12,616 Privacy in Data Systems 2003 PODS 4.1945683e-05
9,336 Small Domain Randomization: Same Privacy, More Utility 2010 VLDB 4.3556432e-05
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
177 Limiting Privacy Breaches in Privacy Preserving Data Mining 2003 PODS 0.0003788711