Secure crowd-sensing protocol for fog-based vehicular cloud

Lewis Nkenyereye, S. M.Riazul Islam, Muhammad Bilal, M. Abdullah-Al-Wadud, Atif Alamri, Anand Nayyar

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

The new paradigm of fog computing was extended from conventional cloud computing to provide computing and storage capabilities at the edge of the network. Applied to vehicular networks, fog-enabled vehicular computing is expected to become a core feature that can accelerate a multitude of services including crowd-sensing. Accordingly, the security and privacy of vehicles joining the crowd-sensing system have become important issues for cyber defense and smart policing. In addition, to satisfy the demand of crowd-sensing data users, fine-grained access control is required. In this paper, we propose a secure and privacy-preserving crowd-sensing scheme for fog-enabled vehicular computing. The proposed architecture is made by a double layer of fog nodes that is used to generate crowd-sensing tasks for vehicles, then collect, aggregate and analyze the data based on user specifications. To ensure data confidentiality and fined-grained access control, we make use of ciphertext-policy attribute-based encryption with access update policy (CP-ABE-UP), which is a well-known one-to-many encryption technique. The policy update algorithm allows the fog nodes to outsource the crowd-sensing data to other fog nodes or to data users directly. We also adopted the ID-based signature tied to pseudonymous techniques to guarantee the authentication and privacy-preservation of the entities in the system. From the upper fog layer to the data user, we show that an information-centric networking (ICN) approach can be applied to maximize the network resources and enhance the security by avoiding unauthorized and unauthenticated data owners. The security analysis confirms that our approach is secure against known attacks, whereas the simulation results show its efficiency in terms of communication with little computational overhead.

Original languageEnglish
Pages (from-to)61-75
Number of pages15
JournalFuture Generation Computer Systems
Volume120
Early online date5 Mar 2021
DOIs
Publication statusPublished - 1 Jul 2021
Externally publishedYes

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