Adaptive and Context-aware Privacy Preservation Exploiting User Interactions in Smart Environments
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. © 2013 Elsevier B.V. All rights reserved.
Recommended Citation
G. Pallapa et al., "Adaptive and Context-aware Privacy Preservation Exploiting User Interactions in Smart Environments," Pervasive and Mobile Computing, vol. 12, pp. 232 - 243, Elsevier, Jan 2014.
The definitive version is available at https://doi.org/10.1016/j.pmcj.2013.12.004
Department(s)
Computer Science
Keywords and Phrases
Context awareness; Pervasive systems; Privacy preservation; Smart environments; User interactions
International Standard Serial Number (ISSN)
1574-1192
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Jan 2014
Comments
National Science Foundation, Grant CNS-1150192