A hybrid optimization approach for anonymizing transactional data

Abstract

Transactional data about individuals is increasingly being collected to support many important real-life applications ranging from healthcare to marketing. Thus, privacy issues in sharing transactional data among different parties have attracted considerable research interest in recent years. Due to the high-dimensionality and sparsity of transactional data, existing privacy-preserving techniques will incur excessive information loss. We propose a hybrid optimization approach for anonymizing transactional data through integrating different anonymous techniques. Experimental results verify that our approach significantly outperforms the current state-of-the-art algorithms in terms of data utility.