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    A spatiotemporal model of obfuscation strategies and counter strategies for location privacy

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    A spatiotemporal model of obfuscation strategies and counter strategies for location privacy (242.2Kb)

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    Author
    DUCKHAM, MATT; KULIK, LARS; Birtley, Athol
    Date
    2006
    Source Title
    Lecture Notes in Computer Science 4197
    University of Melbourne Author/s
    Kulik, Lars; Duckham, Matt; BIRTLEY, ATHOL WOLF
    Affiliation
    Engineering: Department of Geomatics
    Metadata
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    Document Type
    Book Chapter
    Citations
    Duckham, M., Kulik, L. & Birtley, A. (2006). A spatiotemporal model of obfuscation strategies and counter strategies for location privacy. In M. Raubal, H. Miller, A. Frank & M. Goodchild (Eds.), Lecture Notes in Computer Science 4197 (pp. 47-64). Springer.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/33595
    Description

    This is a post-print of an article whose final and definitive form has been published in Lecture Notes in Computer Science © 2006 Springer; the original publication is available at: http://www.springerlink.com

    Abstract
    Safeguarding location privacy is becoming a critical issue in location based services and location-aware computing generally. Two drawbacks of many previous models of location privacy are: 1) the models only consider a person’slocation privacy protection, but not the invasion of location privacy by external agents; and 2) the models are static and do not consider the spatiotemporal aspectsof movement. We argue that, to be complete, any model of location privacy needs to enable the analysis and identification of techniques both to protect and toinvade an individual’s location privacy over time. One way to protect an individual’s location privacy is to minimize the information revealed about a person’s location, termed obfuscation. This paper presents an explicitly spatiotemporalmodel of location privacy that models a third party’s limited knowledge of a mobileindividual’s location. We identify two core strategies that a third party canuse to refine its knowledge, so potentially invading that mobile individual’s locationprivacy. A global refinement strategy uses the entire history of knowledgeabout an agent’s location in a single step. A local refinement strategy iterativelyconstructs refined knowledge over time.We present a formal model of global andlocal refinement operators, and show how this formal model can be translatedinto a computational model in a simulation environment.

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