Statistical agencies use a variety of disclosure control policies with ad hoc justification in disseminating data. The issues involved are clarified here by showing that several of these policies are special cases of a general disclosure-limiting (DL) approach based on predictive distributions and uncertainty functions. A user's information posture regarding a target is represented by one predictive distribution before data release and another predictive distribution after data release. A user's lack of knowledge about the target at any time is measured by an uncertainty function applied either to the current predictive distribution or to the current predictive distribution and the previously held predictive distribution. Common disclosure control policies, such as requiring released cell relative frequencies to be bounded away from both zero and one, are shown to be equivalent to disclosure rules that allow data release only if specific uncertainty functions at particular predictive distributions exceed a limit. Data transformations, such as aggregation and cell suppression, that are intended to reduce the extent of disclosure are analyzed in simple but realistic scenarios.