Abstract
With the advent of connected vehicles, drivers will communicate personal information describing their driving style to their vehicles manufacturers, stakeholders or insurers. These information will serve to evaluate remotely vehicle state via an e-diagnostics service, to provide over-the-air update of vehicles controllers and to offer new third parties services targeting profiled drivers. An inherent problem to all the previous services is privacy. Indeed, the providers of these services will need access to sensitive data in order to propose in return an adequate service.
In this paper, we propose a privacy-preserving k-means clustering for drivers subscribed to the pay how you drive service, where vehicles insurance fees are adjusted according to driving behavior. Our proposal relies on secure multi-party computation and additive homomorphic encryption schemes to ensure the confidentiality of drivers data during clustering and classification.
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Notes
- 1.
- 2.
Note that our protocol is not only limited to drivers clustering and can be easily generalized to cover all use-cases using k-means for clustering.
- 3.
we rewrite only \({S_f}_1\) and \({S_n}_1\) for the sake of clarity.
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Omri, O.E., Boudguiga, A., Izabachene, M., Klaudel, W. (2019). Privacy-Preserving k-means Clustering: an Application to Driving Style Recognition. In: Liu, J., Huang, X. (eds) Network and System Security. NSS 2019. Lecture Notes in Computer Science(), vol 11928. Springer, Cham. https://doi.org/10.1007/978-3-030-36938-5_43
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