A novel two phase data sensitivity based access control framework for healthcare data

This paper proposes a secure data access control framework that utilizes the attribute values and the user specific usage details to provide secure and fine-grained data access. It aims to minimize the data leakage during data retrieval which is a critical challenge for handling health data. No standard data retrieval policies are in place for preserving the privacy of medical data though any data breach may have a disastrous effect on society. In our proposed framework data is divided into different segments based on data sensitivity and data utility and users are authorized based on attribute details. An Integer Linear Programming (ILP) based solution is designed here to optimize the amount of information a user can retrieve from the application while minimizing the data leakage. The data storage technique and the user authorization technique complement each other to decide upon access to the portion of the information available to that particular user. An experimental result shows the sensitivity score calculation of the data items, ILP based privilege value assignment of the users and data retrieval strategy to minimize data leakage. The technique is validated on the benchmark datasets. The results show the utility of the sensitivity score of the data items and user privilege values while ensuring faster data retrieval time as compared to state-of-the-art works.