Geometric data perturbation for privacy preserving outsourced data mining

  title={Geometric data perturbation for privacy preserving outsourced data mining},
  author={Keke Chen and Ling Liu},
  journal={Knowledge and Information Systems},
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. We 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… CONTINUE READING
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