A privacy framework for personal self-improving smart spaces

N. Liampotis, I. Roussaki, E. Papadopoulou, Yussuf Abu-Shaaban, Howard Williams, N. K. Taylor, S. M. McBurney, K. Dolinar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)


There are various critical privacy issues that need to be addressed in the majority of smart space environments. This paper elaborates on the design of a privacy protection framework for Personal Self-Improving Smart Spaces (PSSs), a concept introduced by the Persist project Consortium. Compared to other smart spaces, such as smart homes and vehicles, this new paradigm provides a truly ubiquitous and fully personalisable user-centric environment. However, the information that needs to be collected, processed and distributed in such an environment is by nature highly privacy-sensitive, as it includes user profile data and preferences, as well as data regarding the past, current and even future user activities and context in general. In this respect, the designed privacy framework aims to address all privacy issues that arise by providing facilities which support multiple digital identities of PSS owners and privacy preferences for deriving privacy policies based on the context and the trustworthiness of the third parties that interact with PSSs. © 2009 IEEE.

Original languageEnglish
Title of host publicationProceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009 - 2009 IEEE International Conference on Privacy, Security, Risk, and Trust, PASSAT 2009
Number of pages6
Publication statusPublished - 2009
Event2009 IEEE International Conference on Privacy, Security, Risk, and Trust - Vancouver, BC, Canada
Duration: 29 Aug 200931 Aug 2009


Conference2009 IEEE International Conference on Privacy, Security, Risk, and Trust
Abbreviated titlePASSAT 2009
CityVancouver, BC


  • Digital identity management
  • Personal smart space
  • Privacy
  • Privacy policy negotiation
  • Privacy preferences
  • Trust evaluation
  • Trust inference


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