A Framework for the Automatic Discovery of Policy from Healthcare Access Logs
Paulett, John Michael
Healthcare organizations are often stymied in their efforts to prevent insider attacks that violate patient privacy. Numerous high-profile privacy breaches involving celebrities have brought this deficiency to the public's attention. In response, recent legislation aims to improve this situation by means of regulations and sanctions. While the public and government may demand more privacy safeguards, the current state-of-the-art tools in healthcare security, such as access control and auditing, will still be limited in their ability to solve the issue technically. These technologies are theoretically sound and tested in other industries, yet are suboptimal because no feasible methods exist for generating the policies these systems must act upon, due to the inherent complexities of modern healthcare organizations. To address this shortcoming, we present a novel open-source framework, which mines low-level statistics of how users interact within the organization from the access logs of the organization's information systems. Our framework is scalable—capable of handling real world data integrity issues. We demonstrate the use of our tool by modeling the Vanderbilt University Medical Center. Additionally, we compare our framework's model to traditional experts who would attempt to manually generate a similar model.