1st International ICST Workshop on the Value of Security through Collaboration

Research Article

Privacy preserving ubiquitous service provisioning based on Bayesian network conversion

  • @INPROCEEDINGS{10.1109/SECCMW.2005.1588298,
        author={Hiroyuki  Kasai and Shoji  Kurakake and Wataru  Uchida},
        title={Privacy preserving ubiquitous service provisioning based on Bayesian network conversion},
        proceedings={1st International ICST Workshop on the Value of Security through Collaboration},
        publisher={IEEE},
        proceedings_a={SECOVAL},
        year={2006},
        month={2},
        keywords={},
        doi={10.1109/SECCMW.2005.1588298}
    }
    
  • Hiroyuki Kasai
    Shoji Kurakake
    Wataru Uchida
    Year: 2006
    Privacy preserving ubiquitous service provisioning based on Bayesian network conversion
    SECOVAL
    IEEE
    DOI: 10.1109/SECCMW.2005.1588298
Hiroyuki Kasai1,*, Shoji Kurakake1, Wataru Uchida1
  • 1: NTT DoCoMo, Japan
*Contact email: kasai@netlab.nttdocomo.co.jp

Abstract

Protecting personal privacy is already seen as a crucial requirement in the implementation of service provisioning in the ubiquitous environment. From the view point of preserving personal privacy, the simplest approach would be for users not to reveal any kind of private information at any time while keeping the number of available services unrestricted. Meanwhile, from the service provider's point of view, though this has been not clearly stated so far, their service logics should also be hidden from others because those logics may leak their know-how. This paper presents an ubiquitous service provisioning mechanism that gives more opportunities for users to get available services while preserving the secrecy of users' and providers' sensitive information. The basic idea of this mechanism is to share service execution procedures between the service provider and the user by exchanging converted service logic described in the form of Bayesian decision networks. This paper describes the proposed mechanism and the conversion algorithm for the Bayesian networks, and details the system architecture and implementation.