Research Article Privacy-Aware Data Forensics of VRUs Using Machine Learning and Big Data Analytics Muhammad Babar , 1 Muhammad Usman Tariq, 2 Ahmed S. Almasoud, 3 and Mohammad Dahman Alshehri 4 1 Department of Computer Science, Allama Iqbal Open University, Islamabad, Pakistan 2 Abu Dhabi School of Management, Abu Dhabi, UAE 3 College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia 4 Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia CorrespondenceshouldbeaddressedtoMuhammadBabar;muhammad.babar@aiou.edu.pk Received 27 September 2021; Revised 4 November 2021; Accepted 12 November 2021; Published 28 November 2021 AcademicEditor:FarhanUllah Copyright©2021MuhammadBabaretal.isisanopenaccessarticledistributedundertheCreativeCommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. epresentspreadingoutofbigdatafoundtherealizationofAIandmachinelearning.Withtheriseofbigdataandmachine 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. e existingarchitectureshavevariouschallenges,wheredataprivacyistheforemostchallengeforvulnerableroadusers(VRUs).e keyreasonforfailureintrafficcontrolforpedestriansisflawedintheprivacyhandlingoftheusers.euserdataareatriskandare 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 learningtoanalyzeandprocessbigdataefficientlyinasecureenvironment.eproposedmodelconsiderstheprivacyofusers during big data processing. e proposed architecture is a layered framework with a parallel and distributed module using machine learning on big data to achieve secure big data analytics. e proposed architecture designs a distinct unit for privacy managementusingamachinelearningclassifier.Astreamprocessingunitisalsointegratedwiththearchitecturetoprocessthe information.eproposedsystemisapprehendedusingreal-timedatasetsfromvarioussourcesandexperimentallytestedwith reliabledatasetsthatdisclosetheeffectivenessoftheproposedarchitecture.edataingestionresultsarealsohighlightedalong with training and validation results. 1. Introduction In a recent technological globe, data are mounting rapidly, andhumansaremostlyrelyingondata.Besidesthepaceat whichthedatarise,itisbecomingimpracticabletostockup the data into any specific server. Today the planet holds an enormous quantity of data that persists to grow exponen- tially at very high speed and is insecure [1]. Moreover, the entire globe has gone online with the invention of the web, and every single action we do puts down a digital map out that is prone to vulnerability [2]. With the rise of big data andmachinelearning,thenotionofimprovingaccuracyand enhancing the efficacy of AI projects is also gaining im- portanceandislargelyrecognized[3].Someofthesefactors oftheevolutionofdataaretheenhancementoftechnology, socialmedia,andInternetofings(IoT).IoTisoneofthe latestconceptsinthecurrentagethatismostlyapplicablein trafficcontrollingandmonitoringapplications.efutureof this globe is secure IoT that will be going to alter today’s world objects into intelligent and smart objects [4]. Smart systems include IoTdevices, such as sensors and actuators, process input connectivity, and people. Sensors and Hindawi Security and Communication Networks Volume 2021, Article ID 3320436, 9 pages https://doi.org/10.1155/2021/3320436