On Unifying Privacy and Uncertain Data Models

The problem of privacy-preserving data mining has been studied extensively in recent years because of the increased amount of personal information which is available to corporations and individuals. Most privacy transformations use some form of data perturbation or representational ambiguity in order to reduce the risk of identification. The final results from privacy transformation methods often require the underlying applications to be modified in order to work with the new representation of the data. Since the end results of privacy-transformation methods have not been standardized, the required modifications may vary with the method used for the privacy transformation. In some cases, it can be an enormous effort to re-design applications to work with the anonymized data. While the results of privacy-transformation methods are a natural form of uncertain data, the two problems have generally been studied independently. In this paper, we make a first attempt to unify the two fields, and propose a privacy transformation for which existing uncertain data management tools can be directly used. This is a great advantage, since it means that the wide spectrum of research available for uncertain data management can also be used for privacy-preserving data mining. We propose an uncertain version of the k-anonymity model which is related to the well known deterministic model of k- anonymity. The uncertain version of the k-anonymity model has the additional feature of introducing greater uncertainty for the adversary over an equivalent deterministic model. As specific instantiations of this approach, we test the effectiveness of the privacy transformation on the problems of query estimation and classification, and show that the technique retains greater accuracy than other k-anonymity models.