Adaptive and context-aware privacy preservation schemes exploiting user interactions in pervasive environments

In a pervasive system, users have very dynamic and rich interactions with the environment and its elements, including other users. To efficiently support users in such environments, a high-level representation of the system (namely, context) is usually exploited. However, since pervasive environments are inherently people-centric, context might consist of sensitive information. As a consequence, privacy concerns arise, especially in terms of how to control information disclosure to third parties (e.g., other users). In this paper we propose context-aware approaches to privacy preservation in wireless and mobile pervasive environments. Specifically, we design two schemes: (i) to reduce the interactions between the user and the system, and (ii) to exploit the interactions between different users. Both of our solutions are adaptive, thus suitable for dynamic scenarios. In addition, our schemes require limited computational and storage resources, so that they can be implemented on resource-constrained personal and sensing devices. We apply our solutions to a smart healthcare scenario, and show that our schemes not only effectively protect the user privacy, but also significantly reduce the interactions with the system, thus improving the user experience.