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Towards Attack-Resilient Geometric Data Perturbation
Proceedings of the Seventh SIAM International Conference on Data Mining
  • Keke Chen, Wright State University - Main Campus
  • Ling Liu
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Data perturbation is a popular technique for privacy-preserving 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 preserving the important geometric properties of a multidimensional dataset, while providing better privacy guarantee for data classification modeling. The preliminary study has shown that random geometric perturbation can well preserve model accuracy for several popular classification models, including kernel methods, linear classifiers, and SVM classifiers, while it also revealed some security concerns to random geometric perturbation. In this paper, we address some potential attacks to random geometric perturbation and design several methods to reduce the threat of these attacks. Experimental study shows that the enhanced geometric perturbation can provide satisfactory privacy guarantee while still well preserving model accuracy for the discussed data classification models.

Paper presented at the 2007 Society for Industrial and Applied Mathematics International Conference on Data Mining, Minneapolis, MN, April 26-28, 2007.

Citation Information
Keke Chen and Ling Liu. "Towards Attack-Resilient Geometric Data Perturbation" Proceedings of the Seventh SIAM International Conference on Data Mining (2007) ISSN: 9780898716306
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