Mobility patterns of vehicles and people provide powerful data sources for location-based services such as fleet optimization and traffic flow analysis . Existing privacy measures for non-sequential data are not suitable for trajectory data and this paper provides an answer to this issue . We introduce a model of an adversary with imperfect knowledge that is based on the concept of equivalence classes . We then adapt standard privacymeasures, i.e. k-anonymity, l-diversity and t-closeness to the peculiarities oftrajectory data . Our approach to measuring trajectory privacy provides ageneral measure, independent of whether and what anonymization has been applied, which can be used to intuitively compare privacy of differentdatasets . This work is of high relevance to all service providers acting asprocessors of trajectory data who want to manage privacy risks and optimize the privacy of their services .

Author(s) : Stefano Bennati, Aleksandra Kovacevic

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Keywords : data - privacy - trajectory - based - anonymity -

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