Class Restricted Clustering and Micro-Perturbation for Data Privacy.

The extensive use of information technologies by organizations to collect and share personal data has raised strong privacy concerns. To respond to the public's demand for data privacy, a class of clustering-based data masking techniques is increasingly being used for privacy-preserving data sharing and analytics. Traditional clustering-based approaches for masking numeric attributes, while addressing re-identification risks, typically do not consider the disclosure risk of categorical confidential attributes. We propose a new approach to deal with this problem. The proposed method clusters data such that the data points within a group are similar in the non-confidential attribute values whereas the confidential attribute values within a group are well distributed. To accomplish this, the clustering method, which is based on a minimum spanning tree (MST) technique, uses two risk-utility tradeoff measures in the growing and pruning stages of the MST technique respectively. As part of our approach we also propose a novel cluster-level micro-perturbation method for masking data that overcomes a common problem of traditional clustering-based methods for data masking, which is their inability to preserve important statistical properties such as the variance of attributes and the covariance across attributes. We show that the mean vector and the covariance matrix of the masked data generated using the micro-perturbation method are unbiased estimates of the original mean vector and covariance matrix. An experimental study on several real-world datasets demonstrates the effectiveness of the proposed approach.