HALT: Hybrid anonymization of longitudinal transactions

The objective of this study is to develop a privacy-preserving framework for publishing longitudinal health data. Longitudinal health data contain valuable clinical information about patients collected over time and there is an increasing demand to use these data in medical and clinical research. However, since longitudinal health data contain personal information, improper release and usage of such data may violate privacy of patients. In this paper, we study the challenges of publishing longitudinal health data and propose a privacy notion, called (K,C)^P-privacy together with a hybrid anonymization algorithm. This work is the first attempt to anonymize multidimensional longitudinal data to prevent both identity disclosure and attribute disclosure. Experimental results on the synthetic data demonstrate the effectiveness of our approach.