Abstract: A method and system for ensuring statistical disclosure limitation (SDL) of categorical or continuous micro data, while maintaining the analytical quality of the micro data. The new SDL methodology exploits the analogy between (1) taking a sample (instead of a census,) along with some adjustments, including imputation, for missing information, and (2) releasing a subset, instead of the original data set, along with some adjustments for records still at disclosure risk. Survey sampling reduces monetary cost in comparison to a census, but entails some loss of information. Similarly, releasing a subset reduces disclosure cost in comparison to the full database, but entails some loss of information. Thus, optimal survey sampling methods can be used for statistical disclosure limitation. The method includes partitioning the database into risk strata, optimal probabilistic substitution, optimal probabilistic subsampling, and optimal sampling weight calibration.

Abstract: A method and system for ensuring statistical disclosure limitation (SDL) of categorical or continuous micro data, while maintaining the analytical quality of the micro data. The new SDL methodology exploits the analogy between (1) taking a sample (instead of a census,) along with some adjustments, including imputation, for missing information, and (2) releasing a subset, instead of the original data set, along with some adjustments for records still at disclosure risk. Survey sampling reduces monetary cost in comparison to a census, but entails some loss of information. Similarly, releasing a subset reduces disclosure cost in comparison to the full database, but entails some loss of information. Thus, optimal survey sampling methods can be used for statistical disclosure limitation. The method includes partitioning the database into risk strata, optimal probabilistic substitution, optimal probabilistic subsampling, and optimal sampling weight calibration.