Simulation-based Approaches for Privacy Preservation

Privacy preservation is a fundamental issue in data mining and knowledge discovery. The primary objective of privacy preservation is to protect an individual’s confidential information in released data sets. In recent years, several simulation-based approaches for privacy preservation have been proposed. The idea is to generate a synthetic data set with the constraint that the probability distribution is as close as possible to that of the original set. In this paper, we propose two frameworks for simulation-based privacy preservation of multivariate numerical data. The first framework, called PRIMP (PRivacy preserving by Independent coMPonents), is based on independent component analysis (ICA). It is shown empirically that PRIMP outperforms other simulation-based approaches in terms of Spearman’s rank correlation and Kendall’s tau correlation. In addition, we prove that the synthetic data generated by PRIMP is sufficiently different from the original data; thus, we are able to protect confidential information in the original data. Also, PRIMP is easy to implement and very effective because, by using FastICA, its run time is linear to the size of the input. The second approach proposed is a hybrid method that combines PRIMP and Cholesky’s decomposition technique. It is shown empirically that the hybrid method preserves the covariance matrix of the original data exactly. The method also resolves the problem of generating good seeds for the Cholesky-based approach. Although, the empirical results show that the hybrid approach is not always better than the PRIMP in terms of Spearman’s rank correlation and Kendall’s tau correlation, in theory, the risk of information leakage under the hybrid approach is less than that under PRIMP.