Trust, Security and Privacy for Big Data

Data has revolutionized the digital ecosystem. Readily available large datasets foster AI and machine learning automated solutions. The data generated from diverse and varied sources including IoT, social platforms, healthcare, system logs, bio-informatics, etc. contribute to and define the ethos of Big Data which is volume, velocity and variety. Data lakes formed by the amalgamation of data from these sources requires powerful, scalable and resilient storage and processing platforms to reveal the true value hidden inside this data mine. Data formats and its collection from various sources not only introduce unprecedented challenges to different domains including IoT, manufacturing, smart cars, power grids etc., but also highlight the security and privacy issues in this age of big data. Security and privacy in big data is facing many challenges, such as generative adversary networks, efficient encryption and decryption algorithms, encrypted information retrieval, attribute-based encryption, attacks on availability, and reliability. Providing security and privacy for big data storage, transmission, and processing have been attracting much attention in all big data related areas. The book provides timely and comprehensive information for researchers and industry partners in communications and networking domains to review the latest results in security and privacy related work of Big Data. It will serve computer science and cybersecurity communities including researchers, academicians, students, and practitioners who have interest in big data trust privacy and security aspects. It is a comprehensive work on the most recent developments in security of datasets from varied sources including IoT, cyber physical domains, big data architectures, studies for trustworthy computing, and approaches for distributed systems and big data security solutions etc.

Table of Contents:
1. DigImoPriv: A Big Data Framework for Preserving Privacy of Digital Immortals 2. Federated Learning Role in Big Data, Iot Services and Applications Security, Privacy and Trust in Iot: A Survey 3. From the Cloud to the Edge: Towards a Distributed and Light Weight Secure Big Data Pipelines for IoT Applications 4. Ground Point Filtering and Digital Terrain Model Generation using LiDAR Data 5. Predictive Big Data Analytics and Privacy based Decision Support System 6. Fingerprinting Based Positioning Techniques Using Machine Learning Algorithms: Principles, Approaches and Challenges 7. Recent Advancements in Network and Cyber Security using RNN 8. A Big Data Framework for Dynamic Consent 9. A Low-Level Hybrid Intrusion Detection System Based on Hardware Performance Counters 10. Comparative Study on Machine Learning Methods to Detect Metamorphic Threats

About the Author :
Mamoun Alazab is an associate professor at the College of Engineering, IT and Environment at Charles Darwin University, Australia. He received his Ph.D. degree in Computer Science from the Federation University of Australia, School of Science, Information Technology and Engineering. He is a cybersecurity researcher and practitioner with industry and academic experience. Alazab’s research is multidisciplinary that focuses on cybersecurity and digital forensics of computer systems with a focus on cybercrime detection and prevention. He has more than 150 research papers in many international journals and conferences. He works closely with government and industry on many projects, including Northern Territory (NT) Department of Information and Corporate Services, IBM, Trend Micro, the Australian Federal Police (AFP), the Australian Communications and Media Authority (ACMA), Westpac, United Nations Office on Drugs and Crime (UNODC), and the Attorney General’s Department. He is a senior member of the IEEE. He is the founding chair of the IEEE Northern Territory (NT) Subsection. Maanak Gupta is an assistant professor in Department of Computer Science at Tennessee Tech University, USA. He received his Ph.D. in Computer Science from the University of Texas at San Antonio and has worked as a postdoctoral research fellow at the Institute for Cyber Security. He also holds an M.S. degree in Information Systems from Northeastern University, Boston. His primary area of research includes security and privacy in cyber space focused in studying foundational aspects of access control and their application in technologies including cyber physical systems, cloud computing, IoT and Big data. Dr Gupta has worked in developing novel security mechanisms, models and architectures for next generation smart cars, smart cities, intelligent transportation systems and smart farming. He is also interested in machine learning based malware analysis and AI assisted cyber security solutions. His scholarly work is regularly published at top peer-reviewed security venues including ACM SIGSAC conferences and refereed journals. His research has been funded by the US National Science Foundation (NSF), NASA, US Department of Defense (DoD) and private industry. He is also the co-founder and co-chair of SMARTFARM-2020 and SaT-CPS 2021 workshops. In addition, he is a reviewer for several journals and conferences.