Adaptive and context-aware privacy preservation exploiting user interactions in smart 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, called the 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 other users and third parties. In this article, we propose context-aware approaches to privacy preservation in wireless and mobile pervasive environments. Specifically, we design two schemes: (i) to reduce the number of interactions between the user and the system; and (ii) to exploit the interactions between different users. Both solutions are adaptive and, thus, suitable for dynamic scenarios. In addition, our schemes require limited computational and storage resources. As a consequence, they can be easily implemented on resource-constrained personal communication and sensing devices. We apply our solutions to a smart workplace scenario and show that our schemes protect user privacy while significantly reducing the interactions with the system, thus improving the user experience.