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Data perturbation is a popular technique in privacy-preserving data mining. A major challenge in data perturbation is to balance privacy protection and data utility, which are normally considered as a pair of conflicting factors. The authors argue that selectively preserving the task/model specific information in perturbation will help achieve better privacy guarantee and better data utility. One type of such information is the multidimensional geometric information, which is implicitly utilized by many data mining models. To preserve this information in data perturbation, they propose the Geometric Data Perturbation (GDP) method. In this paper, they describe several aspects of the GDP method.
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