A Privacy Recommending Data Processing Model for Internet of Vehicles (IoV) Services
Received: 4 May 2024 | Revised: 25 May 2024 and 29 May 2024 | Accepted: 30 May 2024 | Online: 29 June 2024
Corresponding author: Ali Alqarni
Abstract
The Internet of Vehicles (IoV) faces security challenges in maintaining privacy due to the presence of open networks and diverse services. Ensuring privacy is essential in transportation networks to provide users with a long-lasting driving, navigation, and communication experience. In this paper, the proposed Privacy Recommending Data Processing Model (PRDPM) is deployed to handle the huge amount of data accumulated in this field. The proposed model adopts data processing techniques that are dependent on user demand and are influenced by either neighboring entities or service providers. The various application requirements are analyzed to minimize the potential privacy consequences. The data from various intervals are utilized to validate the parameters in the operational plane. Thus, data balancing is performed using plane differentiation to prevent privacy leaks in either of the vehicular services. This is useful for neighbors and infrastructures across various applications/users.
Keywords:
Internet of Things (IoT), privacy-preserving, privacy leakDownloads
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