Privacy modelling and management for assisted living within smart homes

Ambient Assisted Living (AAL) technologies create intelligent systems to assist the aging population for a healthier and safer life in their living environment. Such systems usually offer context-aware, personalized and adaptive services. However, these kinds of systems make extensive and intensive use of personal data, which makes privacy protection a critical issue. In this paper, we propose a framework for privacy modeling computation and management for AAL within Smart Homes. We analyze the privacy features in the smart home that affect the privacy of the users. Based on these features a metric is developed to compute the sensitivity of the collected information and consequently the potential privacy risk. A simple implementation of the proposed framework is then applied to a real world smart home living environment at Great Northern Haven, in which data were collected and the framework was evaluated. This study offers an effective and practical approach to evaluate the privacy risk of users and proposes a metric that can be used for access control and recommendation of privacy settings to the users of the AAL environments.