000223394 001__ 223394
000223394 005__ 20190416220334.0
000223394 037__ $$aCONF
000223394 245__ $$aPrivateRide: A Privacy-Enhanced Ride-Hailing Service
000223394 269__ $$a2017
000223394 260__ $$c2017
000223394 336__ $$aConference Papers
000223394 520__ $$aIn the past few years, we have witnessed a rise in the popularity of ride-hailing services (RHSs), an online marketplace that enables accredited drivers to use their own cars to drive ride-hailing users. Unlike other transportation services, RHSs raise significant privacy concerns, as providers are able to track the precise mobility patterns of millions of riders worldwide. We present the first survey and analysis of the privacy threats in RHSs. Our analysis exposes high-risk privacy threats that do not occur in conventional taxi services. Therefore, we propose PrivateRide, a privacy-enhancing and practical solution that offers anonymity and location privacy for riders, and protects drivers’ information from harvesting attacks. PrivateRide lowers the high-risk privacy threats in RHSs to a level that is at least as low as that of many taxi services. Using real data-sets from Uber and taxi rides, we show that PrivateRide significantly enhances riders’ privacy, while preserving tangible accuracy in ride matching and fare calculation, with only negligible effects on convenience. Moreover, by using our Android implementation for experimental evaluations, we show that PrivateRide’s overhead during ride setup is negligible. In short, we enable privacy-conscious riders to achieve levels of privacy that are not possible in current RHSs and even in some conventional taxi services, thereby offering a potential business differentiator.
000223394 6531_ $$aride-hailing
000223394 6531_ $$alocation privacy
000223394 700__ $$0247980$$g237046$$aPham, Thi Van Anh
000223394 700__ $$0248434$$g250946$$aDacosta Petrocelli, Italo Ivan
000223394 700__ $$aJacot-Guillarmod, Bastien
000223394 700__ $$aHuguenin, Kévin
000223394 700__ $$aHajar, Taha
000223394 700__ $$aTramèr, Florian
000223394 700__ $$aGligor, Virgil
000223394 700__ $$0240456$$g105427$$aHubaux, Jean-Pierre
000223394 7112_ $$d2017$$cMinneapolis, USA$$a17th Privacy Enhancing Technologies Symposium (PETS)
000223394 773__ $$tProc. of 17th Privacy Enhancing Technologies Symposium (PETS)
000223394 8564_ $$uhttps://infoscience.epfl.ch/record/223394/files/paper.pdf$$zn/a$$s748313$$yn/a
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000223394 937__ $$aEPFL-CONF-223394
000223394 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
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