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Theses

Privacy-enhancing technologies for ridesharing

Abstract : The emergence of mobile phones and connected objects has profoundly changed our daily lives. These devices, thanks to the multitude of sensors they embark, allow access to a broad spectrum of services. In particular, position sensors have contributed to the development of location-based services such as navigation, ridesharing, real-time congestion tracking... Despite the comfort offered by these services, the collection and processing of location data seriously infringe the privacy of users. In fact, these data can inform service providers about points of interests (home, workplace, sexual orientation), habits and social network of the users. In general, the protection of users' privacy can be ensured by legal or technical provisions. While legal measures may discourage service providers and malicious individuals from infringing users' privacy rights, the effects of such measures are only observable when the offense is already committed and detected. On the other hand, the use of privacy-enhancing technologies (PET) from the design phase of systems can reduce the success rate of attacks on the privacy of users. The main objective of this thesis is to demonstrate the viability of the usage of PET as a means of location data protection in ridesharing services. This type of location-based service, by allowing drivers to share empty seats in vehicles, helps in reducing congestion, CO2 emissions and dependence on fossil fuels. In this thesis, we study the problems of synchronization of itineraries and matching in the ridesharing context, with an explicit consideration of location data (origin, destination) protection constraints. The solutions proposed in this thesis combine multimodal routing algorithms with several privacy-enhancing technologies such as homomorphic encryption, private set intersection, secret sharing, secure comparison of integers. They guarantee privacy properties including anonymity, unlinkability, and data minimization. In addition, they are compared to conventional solutions, which do not protect privacy. Our experiments indicate that location data protection constraints can be taken into account in ridesharing services without degrading their performance.
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  • HAL Id : tel-01735575, version 2

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Ulrich Matchi Aïvodji. Privacy-enhancing technologies for ridesharing. Cryptography and Security [cs.CR]. Université Paul Sabatier - Toulouse III, 2018. English. ⟨NNT : 2018TOU30017⟩. ⟨tel-01735575v2⟩

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