A general framework for privacy preserving data publishing

Data publishing is an easy and economic means for data sharing, but the privacy risk is a major concern in data publishing. Privacy preservation is a major task in data sharing for organizations like bureau of statistics, and hospitals. While a large number of data publishing models and methods have been proposed, their utility is of concern when a high privacy requirement is imposed. In this paper, we propose a new framework for privacy preserving data publishing. We cap the belief of an adversary inferring a sensitive value in a published data set to as high as that of an inference based on public knowledge. The semantic meaning is that when an adversary sees a record in a published data set, s/he will have a lower confidence that the record belongs to a victim than not. We design a method integrating sampling and generalization to implement the model. We compare the method with some state-of-the-art methods on privacy-preserving data publishing experimentally, our proposed method provides sound semantic protection of individuals in data and, provides higher data utility.