Maximum Likelihood Postprocessing for Differential Privacy under Consistency Constraints

Abstract

When analyzing data that has been perturbed for privacy reasons, one is often concerned about its usefulness. Recent research on differential privacy has shown that the accuracy of many data queries can be improved by post-processing the perturbed data to ensure consistency constraints that are known to hold for the original data. Most prior work converted this post-processing step into a least squares minimization problem with customized efficient solutions. While improving accuracy, this approach ignored the noise distribution in the perturbed data.