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Privacy is an increasingly important aspect of data publishing services. If personal private information is leaked from the data, the service will be regarded as unacceptable by the original owners of the data. Two different approaches to defining a notion of database privacy, the generalization method and the perturbation method, have been independently studied. These two approaches have significantly differences, making it hard to compare related research. In this paper, the authors propose a unified model that is based on the perturbation method, but which is applicable to generalized data sets. In particular, this model applies the notion of differential privacy to data sets that satisfy k-anonymity.
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