Privacy-Aware Data Forensics of VRUs Using Machine Learning and Big Data Analytics

Abstract

The present spreading out of big data found the realization of AI and machine learning. With the rise of big data and machine learning, the idea of improving accuracy and enhancing the efficacy of AI applications is also gaining prominence. Machine learning solutions provide improved guard safety in hazardous traffic circumstances in the context of traffic applications. The existing architectures have various challenges, where data privacy is the foremost challenge for vulnerable road users (VRUs). The key reason for failure in traffic control for pedestrians is flawed in the privacy handling of the users. The user data are at risk and are prone to several privacy and security gaps. If an invader succeeds to infiltrate the setup, exposed data can be malevolently influenced, contrived, and misrepresented for illegitimate drives. In this study, an architecture is proposed based on machine learning to analyze and process big data efficiently in a secure environment. The proposed model considers the privacy of users during big data processing. The proposed architecture is a layered framework with a parallel and distributed module using machine learning on big data to achieve secure big data analytics. The proposed architecture designs a distinct unit for privacy management using a machine learning classifier. A stream processing unit is also integrated with the architecture to process the information. The proposed system is apprehended using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The data ingestion results are also highlighted along with training and validation results.

Empowering Pedestrian Safety: Unveiling a Lightweight Scheme for Improved Vehicle-Pedestrian Safety

“…Finally, although VRU privacy and data security are essential issues, they are beyond the scope of this article. We do, however, briefly describe one attempt from [40], in which the authors use machine learning classifiers to provide a framework for the safe processing of massive amounts of data. Figure 9 summarizes the research directions and trends for V2P design.…”

Machine-Learning Approach for Predicting Harmful Diseases using Big Data and IoT: A Review

“…In this paper, a machine learning-based architecture is suggested for effectively analysing and processing massive data in a secure setting. [4] A customer medical data exchange plan for privacy-preserving computer vision is put forth by Wang 2022 et al Our approach combines blockchain technology with a trusted computing environment to make sure that users' control and ownership of their data are not compromised when it is shared. It is suggested to employ a blockchain-based noninteractive secret sharing system so that only users and the TEE may decode shared data.…”

Vehicle to Pedestrian Systems: Survey, Challenges and Recent Trends

“…Finally, while the topic of VRU privacy and data protection is significantly important, it is beyond the scope of this paper. However, we briefly outline one effort in [134] where authors used machine learning classifiers to create an architecture for big data processing in a secure environment. Recent trends are summarized in Table IV.…”

“…Protecting VRU privacy Architecture based on machine learning to facilitate analysis of big data in a secure environment [134] This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication.…”