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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
3623
Venue
SIGMOD
Year
2005
Pagerank
5.8382391e-05
Overall Rank
4,905 | 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 0.00074213516
148 On the Design and Quantification of Privacy Preserving Data Mining Algorithms 2001 PODS 0.00041196325
177 Limiting Privacy Breaches in Privacy Preserving Data Mining 2003 PODS 0.00037858416
559 Maintaining Data Privacy in Association Rule Mining 2002 VLDB 0.0002012549
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3,178 Bayesian Differential Privacy on Correlated Data 2015 SIGMOD 7.4350161e-05
4,795 Optimal Random Perturbation at Multiple Privacy Levels 2009 VLDB 5.9104664e-05
12,625 Privacy in Data Systems 2003 PODS 4.1905499e-05
9,341 Small Domain Randomization: Same Privacy, More Utility 2010 VLDB 4.351469e-05
40 Privacy-Preserving Data Mining 2000 SIGMOD 0.00074213516
148 On the Design and Quantification of Privacy Preserving Data Mining Algorithms 2001 PODS 0.00041196325
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