M-generalization for multipurpose transactional data publication

Abstract

Transactional data collection and sharing currently face the challenge of how to prevent information leakage and protect data from privacy breaches while maintaining high-quality data utilities. Data anonymization methods such as perturbation, generalization, and suppression have been proposed for privacy protection. However, many of these methods incur excessive information loss and cannot satisfy multipurpose utility requirements. In this paper, we propose a multidimensional generalization method to provide multipurpose optimization when anonymizing transactional data in order to offer better data utility for different applications. Our methodology uses bipartite graphs with generalizing attribute, grouping item and perturbing outlier. Experiments on real-life datasets are performed and show that our solution considerably improves data utility compared to existing algorithms.