Preserving privacy appears to conflict with providing information. Statistical information can, however, be provided while preserving a specified level of confidentiality protection. The general approach is to provide disclosure-limited data that maximizes its statistical utility subject to confidentiality constraints. Disclosure limitation based on Markov chain methods that respect the underlying uncertainty in real data is examined. For use with categorical data tables a method called Markov perturbation is proposed as an extension of the PRAM method of Kooiman, Willenborg, and Gouweleeuw (1997). Markov perturbation allows cross-classified marginal totals to be maintained and promises to provide more information than the commonly used cell suppression technique.
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