Abstract
In today’s working and shopping environment a lot of sources are present that collect data of people located in those environments. The data gathered by devices such as video cameras, RFID tags, use of credit cards etc. can be combined in order to deduce information which cannot be “measured” directly. In this paper we introduce deduction rules that help to describe which information can be inferred from which sources. Using these rules all information that can be gathered by a pervasive system can be identified and linked to the sources of the raw input data. By that the pervasive system is represented as an information flow graph. In order to enhance privacy we use this graph to determine the data sources, e.g. video cameras or RFID tags, that need to be switched off to adapt a given system to privacy requirements of a certain person. Due to the fact that we do not consider an individual device a data source but cluster those devices into a single source of a certain type, our approach scales well even for large sensor networks. Our algorithms used to build and analyze the information flow graph offer low calculation complexities. Thus, they are well suited to be executed on mobile devices giving the end user back some control over her/his data. Even if she/he cannot influence the system, she/he at least knows which information is exposed to others.
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Ortmann, S., Langendörfer, P., Maaser, M. (2007). Enhancing Privacy by Applying Information Flow Modelling in Pervasive Systems. In: Meersman, R., Tari, Z., Herrero, P. (eds) On the Move to Meaningful Internet Systems 2007: OTM 2007 Workshops. OTM 2007. Lecture Notes in Computer Science, vol 4806. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76890-6_5
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DOI: https://doi.org/10.1007/978-3-540-76890-6_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76889-0
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