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dc.contributor.authorWagner, Isabelen
dc.date.accessioned2017-09-04T09:12:06Z
dc.date.available2017-09-04T09:12:06Z
dc.date.issued2017-11-16
dc.identifier.citationWagner, I. (2017) Measuring Privacy in Vehicular Networks. 42nd IEEE Conference on Local Computer Networks (LCN). accepted for publication. Singapore: IEEE, Oct. 2017.en
dc.identifier.urihttp://hdl.handle.net/2086/14455
dc.description.abstractVehicular communication plays a key role in near- future automotive transport, promising features like increased traffic safety or wireless software updates. However, vehicular communication can expose driver locations and thus poses important privacy risks. Many schemes have been proposed to protect privacy in vehicular communication, and their effectiveness is usually shown using privacy metrics. However, to the best of our knowledge, (1) different privacy metrics have never been compared to each other, and (2) it is unknown how strong the metrics are. In this paper, we argue that privacy metrics should be monotonic, i.e. that they indicate decreasing privacy for increasing adversary strength, and we evaluate the monotonicity of 32 privacy metrics on real and synthetic traffic with state-of- the-art adversary models. Our results indicate that most privacy metrics are weak at least in some situations. We therefore recommend to use metrics suites, i.e. combinations of privacy metrics, when evaluating new privacy-enhancing technologies.en
dc.language.isoen_USen
dc.publisherIEEEen
dc.titleMeasuring Privacy in Vehicular Networksen
dc.typeConferenceen
dc.identifier.doihttps://doi.org/10.1109/lcn.2017.33
dc.researchgroupCyber Security Centreen
dc.peerreviewedYesen
dc.explorer.multimediaNoen
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/P006752/1en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2017-07-11en
dc.researchinstituteCyber Technology Institute (CTI)en


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