Ownership protection of shape datasets with geodesic distance preservation
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Embedding and Retrieving Private Metadata in Electrocardiograms
Journal of Medical Systems
Rights protection of trajectory datasets with nearest-neighbor preservation
The VLDB Journal — The International Journal on Very Large Data Bases
Right-protected data publishing with hierarchical clustering preservation
Proceedings of the 21st ACM international conference on Information and knowledge management
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Perturbation method is a very important technique in privacy preserving data mining. In this technique, loss of information versus preservation of privacy is always a trade off. The question is, how much are the users willing to compromise their privacy? This is a choice that changes from individual to individual. In this paper, we propose an individually adaptable perturbation model, which enables the individuals to choose their own privacy level. Hence our model provides different privacy guarantees for different privacy preferences. We test our new perturbation model by applying different reconstruction methods to the perturbed data sets. Furthermore, we build decision tree and Naive Bayes classifier models on the reconstructed data sets both for synthetic and real world data sets. For the synthetic data set, our experimental results indicate that our model enables the users to choose their own privacy level without reducing the accuracy of the data mining results. For the real world data sets, we got very interesting results, hence we pose the question of whether the perturbation reconstruction model-based privacy preserving data mining is applicable for real-world data?