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Full Description
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.
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
