Abstract
Location-based services rise high privacy concerns because they make it possible to collect and infer sensitive information from a person’s positions and mobility traces. Many solutions have been proposed to safeguard the users’ privacy, at least to a certain extent. However, they generally lacking convincing experimental validation with real human mobility traces. Large databases of real mobility traces are extremely expensive to build or buy. In this paper, we present HUMsim (Human Urban Mobility Simulator), a generator of synthetic but realistic human traces oriented to the experimental validation of privacy solutions. HUMsim generates trajectories that reflect possibly privacy-sensitive habits of people and that, at the same time, account for constraints deriving from a real map. We also validate the soundness of the produced traces by statistically comparing them to real human traces.
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Acknowledgment
This work has been partially supported by the Tuscany region in the framework of the POR CRO FSE 2007–2013 Asse IV Capitale Umano - project SOcial Sensing (SOS); by the Italian Research Project TENACE (pr. no. 20103P34XC); and by Project PITAGORA, cofinanced by the Regional Government of Tuscany (POR CReO Bando Unico R&S 2012) and the European Regional Development Fund (ERDF).
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© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Giurlanda, F., Perazzo, P., Dini, G. (2015). HUMsim: A Privacy-Oriented Human Mobility Simulator. In: Kanjo, E., Trossen, D. (eds) Sensor Systems and Software. S-CUBE 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 143. Springer, Cham. https://doi.org/10.1007/978-3-319-17136-4_7
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DOI: https://doi.org/10.1007/978-3-319-17136-4_7
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