Abstract In this paper, disclosure risk assessment in Statistical Databases is performed by means of a probabilistic approach; in particular, we consider the problem of auditing databases that support statistical sum/count/mean/max/min queries to protect the privacy of sensitive boolean data. We provide both a theoretical framework for evaluating the disclosure risk and a tool for its control and management.
Quality & Quantity (2016) 50:729-749