ACM Transactions on Database Systems (TODS)
Applied multivariate statistical analysis
Applied multivariate statistical analysis
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
LMI approximation for the Radius of the intersection of ellipsoids: survey
Journal of Optimization Theory and Applications
The Security of Confidential Numerical Data in Databases
Information Systems Research
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On the use of spectral filtering for privacy preserving data mining
Proceedings of the 2006 ACM symposium on Applied computing
Statistical database modeling for privacy preserving database generation
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
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The issue of confidentiality and privacy in general databases has become increasingly prominent in recent years. A key element in preserving privacy and confidentiality of sensitive data is the ability to evaluate the extent of all potential disclosure for such data. This is one major challenge for all existing perturbation or transformation based approaches as they conduct disclosure analysis on the perturbed or transformed data, which is too large, considering many organizational databases typically contain a huge amount of data with a large number of categorical and numerical attributes. Instead of conducting disclosure analysis on perturbed or transformed data, our approach is to build an approximate statistical model first and analyze various potential disclosure in terms of parameters of the model built. As the model learned is the only means to generate data for release, all confidential information which snoopers can derive is contained in those parameters.