Adaptive and context-aware privacy preservation exploiting user interactions in smart environments. Pallapa, G., Das, S., K., Di Francesco, M., & Aura, T. Pervasive and Mobile Computing, 12, 2013.
Adaptive and context-aware privacy preservation exploiting user interactions in smart environments [link]Website  abstract   bibtex   
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.
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 title = {Adaptive and context-aware privacy preservation exploiting user interactions in smart environments},
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 year = {2013},
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 abstract = {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.},
 bibtype = {article},
 author = {Pallapa, Gautham and Das, Sajal K and Di Francesco, Mario and Aura, Tuomas},
 journal = {Pervasive and Mobile Computing}
}

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