Towards Attack-Resilient Geometric Data Perturbation

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 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 geo...

• Type: Conference
• Year: 2007
• Where: SDM
• Authors: Keke Chen, Gordon Sun, Ling Liu
• Keywords: Data Mining, Data Perturbation, Geometric Data Perturbation, Random Geometric Perturbation, SDM 2007