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

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Muhammad Babar ◽  
Muhammad Usman Tariq ◽  
Ahmed S. Almasoud ◽  
Mohammad Dahman Alshehri

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.

2021 ◽  
Vol 10 (3) ◽  
pp. 1-11
Author(s):  
Rajasekhara Mouly Potluri ◽  
Narasimha Rao Vajjhala

The research investigates the risks in adopting and implementing big data analytics in Indian micro, small, and medium enterprises (MSMEs). The researchers outlined a survey questionnaire for accumulating reactions from managers working in 50 Indian micro, small, and medium-sized enterprises on behalf of five vital commercial sectors. The application and use of big data analytics offer several significant problems for small companies as an investment in hardware and software resources are substantial. This study's findings provided experimental evidence on five critical challenges that Indian MSMEs face while adopting and implementing big data analytics: lack of human resources, data privacy and security, shortage of technological resources, deficiency of awareness, and financial implications. This study's findings emphasize the challenges that MSMEs face while leveraging big data analytics benefits. The research outcome will promote MSMEs' organizational leadership in planning and developing short-term and long-term information systems strategies.


2018 ◽  
Vol 15 (3) ◽  
Author(s):  
Blagoj Ristevski ◽  
Ming Chen

Abstract This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various – omics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data. We underline the challenging issues about big data privacy and security. Regarding big data characteristics, some directions of using suitable and promising open-source distributed data processing software platform are given.


2020 ◽  
Vol 102 (913) ◽  
pp. 199-234
Author(s):  
Nema Milaninia

AbstractAdvances in mobile phone technology and social media have created a world where the volume of information generated and shared is outpacing the ability of humans to review and use that data. Machine learning (ML) models and “big data” analytical tools have the power to ease that burden by making sense of this information and providing insights that might not otherwise exist. In the context of international criminal and human rights law, ML is being used for a variety of purposes, including to uncover mass graves in Mexico, find evidence of homes and schools destroyed in Darfur, detect fake videos and doctored evidence, predict the outcomes of judicial hearings at the European Court of Human Rights, and gather evidence of war crimes in Syria. ML models are also increasingly being incorporated by States into weapon systems in order to better enable targeting systems to distinguish between civilians, allied soldiers and enemy combatants or even inform decision-making for military attacks.The same technology, however, also comes with significant risks. ML models and big data analytics are highly susceptible to common human biases. As a result of these biases, ML models have the potential to reinforce and even accelerate existing racial, political or gender inequalities, and can also paint a misleading and distorted picture of the facts on the ground. This article discusses how common human biases can impact ML models and big data analytics, and examines what legal implications these biases can have under international criminal law and international humanitarian law.


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