Towards Attack-Resilient Geometric Data Perturbation

  title={Towards Attack-Resilient Geometric Data Perturbation},
  author={Keke Chen and Gordon Sun and Ling Liu},
Data perturbation is a popular technique for privacypreserving data mining. The major challenge of data perturbation is balancing privacy protection and data quality, which are normally considered as a pair of contradictive factors. We propose that selectively preserving only the task/model specific information in perturbation would improve the balance. Geometric data perturbation, consisting of random rotation perturbation, random translation perturbation, and noise addition, aims at… CONTINUE READING
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