Privacy-Preserving k-means Clustering: an Application to Driving Style Recognition

Omri, Othmane El; Boudguiga, Aymen; Izabachène, Malika; Klaudel, Witold

doi:10.1007/978-3-030-36938-5_43

Cited by 4 publications

(1 citation statement)

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“…They also relied on Paillier's cryptosystem homomorphic properties during the classification phase. In 2019, El Ormi et al, [21] proposed a privacy-preserving 𝑘means clustering for driving style recognition. They relied on multiparty computation for computing the distances to clusters centroids.…”

Section: A Privacy In the Its Contextmentioning

confidence: 99%

Towards Privacy-Preserving Platooning Services by means of Homomorphic Encryption

Quero¹,

Boudguiga²,

Sirdey³

et al. 2023

Proceedings Inaugural International Symposium on Vehicle Security &Amp; Privacy

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Platooning is an upcoming technology which aims at improving transportation by allowing a leading human-driven vehicle to automatically guide multiple trucks to their respective destinations, saving driver time, improving road efficiency and reducing gas consumption. However, efficient linkage of trucks to platoons requires the centralization and processing of businesscritical data which truck operators are not willing to disclose. In order to address these issues, we investigate how homomorphic encryption can be used at the core of a protocol for privately linking a vehicle to a nearby platoon without disclosing its location and destination. Furthermore, we provide experimental results illustrating that such protocols achieve acceptable performances and latencies at practical platoon database scales (serving around 500 simultaneous clients on a single platooning server processor core with sub second latency over databases of up to ≈60000 platoons scattered among over 250 destinations).

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Section: A Privacy In the Its Contextmentioning

confidence: 99%