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Putting the User in Control of the Intelligent Transportation System

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Information Security and Privacy (ACISP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 9722))

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Abstract

Intelligent Transportation Systems (ITS) demonstrate innovative services for different modes of transport and traffic management. They enable users to be better informed and make safer, coordinated, and smarter use of transport networks. However, for such systems to be effective, security and privacy considerations have to be enforced. GPS data can lead to accurate traffic models, but such data can also be used by malicious advisories to gain knowledge about the whereabouts of users. In this paper, we propose a model and related constructs for users to define sharing policies for the data they contribute. The model allows entities to define data sharing policies associated with personal information captured by an ITS application. While ITS applications need to use specific data under various quality and context-based constraints, the user-oriented security policies could specify additional usage constraints, and our solution is an approach to mediate between these two sides. Secondly, we propose an algorithm to manage the trust level in the data contributed by users. The solution is based on the quality parameters of the information disseminated in the system, e.g. spatial whereabouts and timestamp for the transmitted data regarding various events happening in the system, combined with the reputation specific to the sources that transmit data. We evaluate a pilot security implementation of the model under real-world assumptions.

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Notes

  1. 1.

    The proposed security model can be applied to various other ITS applications. For example, data sensed by privately-owned devices could further be exchanged by users interested in social-based applications, such as distributed social networks of traffic participants. Another applicability is the participatory sensing paradigm, where citizen campaign can collect traffic-related data, such as noise or pollution levels, to derive accurate models of potential problems within the city.

  2. 2.

    http://mobiway.hpc.pub.ro.

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Acknowledgment

The research in this paper is supported by national projects MobiWay - Mobility beyond Individualism (PN-II-PT-PCCA-2013-4-0321), and DataWay - Real-time Data Processing Platform for Smart Cities: Making sense of Big Data (PN-II-RU-TE-2014-4-2731).

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Correspondence to Florin Pop .

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Gosman, C., Cornea, T., Dobre, C., Pop, F., Castiglione, A. (2016). Putting the User in Control of the Intelligent Transportation System. In: Liu, J., Steinfeld, R. (eds) Information Security and Privacy. ACISP 2016. Lecture Notes in Computer Science(), vol 9722. Springer, Cham. https://doi.org/10.1007/978-3-319-40253-6_14

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  • DOI: https://doi.org/10.1007/978-3-319-40253-6_14

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