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|Title:||A privacy-preserving sensory data sharing scheme in Internet of Vehicles||Authors:||Kong, Qinglei
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2017||Source:||Kong, Q., Lu, R., Ma, M., & Bao, H. (2019). A privacy-preserving sensory data sharing scheme in Internet of Vehicles. Future Generation Computer Systems, 92, 644-655. doi:10.1016/j.future.2017.12.003||Journal:||Future Generation Computer Systems||Abstract:||Internet of Vehicles (IoV), which enables information gathering and disseminating among vehicles, roadside infrastructures and surrounding environments, has received considerable attention recently. However, the flourishing of IoV still faces several challenges in terms of location privacy preservation, vehicular sensory data collection and vehicular sensory data acquisition. Aiming at these challenges, in this paper, we propose a novel efficient and location privacy-preserving data sharing scheme with collusion resistance in IoV, which enables the collection and distribution of the data captured by vehicular sensors. During the vehicular sensory data collection phase, each vehicle structures the multi-dimensional sensory data captured at different locations and exploits the modified Paillier Cryptosystem to achieve the location privacy-preserving sensory data aggregation, while the data aggregation result of multiple vehicles can be recovered by the trusted central entity. During the vehicular sensory data acquisition phase, the proposed scheme exploits the proxy re-encryption technique to achieve the location privacy-preserving data querying at the network edge, i.e., the vehicles query the RSU instead of the trusted central entity. Numerical analysis are performed to validate the effectiveness of the proposed scheme, i.e., low data querying failure probability. Security analysis and performance evaluations are also carried out to validate the security properties and show the computation and communication efficiency of the proposed scheme.||URI:||https://hdl.handle.net/10356/141507||ISSN:||0167-739X||DOI:||10.1016/j.future.2017.12.003||Rights:||© 2017 Elsevier B.V. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
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