Privacy-Preserving Healthcare Data Modeling Based on Sensitivity and Utility

Huge amount of data is produced and processed in recent data-centric applications. Secure management as well as maintaining privacy of the data is a challenging scenario as data itself store the sensitive data along with other application data. Protecting sensitive data is very challenging as it could not be quantified directly. Here, we formulate a metric sensitivity-score to calculate the sensitivity value of the data attributes in a dataset. Sensitive attributes are segregated carefully to avoid possible data linkage attacks by the legitimate users of the application data. Micro-data format is good for maintaining privacy for sensitive data. However, the utility of the data will decrease exponentially. So here in this paper, the authors try to model the data in such a way that a balance between privacy and utility is maintained. The entire data set is segregated in micro-data format with attributes based on the sensitivity value. A Decision Tree-based classifier is used to label the attributes of a sample healthcare dataset as Sensitive or not. Experiments are also conducted to compare the utility and the privacy factor of the proposed method with other existing data partitioning algorithm.