Combinations of SDC methods for microdata protection
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Class-Restricted Clustering and Microperturbation for Data Privacy
Management Science
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Statistical agencies have conflicting obligations to protect confidential information provided by respondents to surveys or censuses and to make data available for research and planning activities. When the microdata themselves are to be released, in order to achieve these conflicting objectives, statistical agencies apply Statistical Disclosure Limitation (SDL) methods to the data, such as noise addition, swapping or microaggregation. In this paper, several multiplicative noise masking schemes are presented. These schemes are designed to preserve positivity and inequality constraints in the data together with means and covariance matrix.