Privacy Goals and Settings Mediator Model for PHRs

Personal Health Record (PHR) platforms support an extensible ecosystem of third party applications that share health data. Still, support for self-management of privacy in PHR platforms remains primitive and insufficient. Privacy experts can offer users advice to help configure their privacy settings, but there is a lack of tools to support this activity. Our research proposes a model (and the associated tooling) that fills the gap between the end-user privacy intentions and what PHR systems offer as privacy features. We develop a privacy goals and settings mediator model based on an existing agent and goal-oriented modeling approach. Our proposed model is capable of encoding the accumulated privacy knowledge of the privacy experts during design-time, and offers privacy setting options that best match the users´ intentions during run-time. We demonstrate the effectiveness of the model through an example scenario. We also report on qualitative evidence of the acceptance of the model by practitioners, based on interviews with health care privacy experts.