A Bayesian Approach for On-Line Sum/Count/Max/Min Auditing on Boolean Data

We consider the problem of auditing databases that support statistical sum/count/max/min queries to protect the privacy of sensitive information. We study the case in which the domain of the sensitive information is the boolean set. Principles and techniques developed for the privacy of statistical databases in the case of continuous attributes do not always apply here. We provide a probabilistic framework for the on-line auditing and we show that sum/count/min/max queries can be audited by means of a Bayesian network.