Session Title | Trust, Security, and Privacy for Big Data frameworks
The volume of data in the world is increasing exponentially, also has revolutionized the current digital ecosystem. The readily available large datasets foster AI and machine learning automated solutions. However, the data format and its collection from various sources introduce unprecedented challenges to different domains including IoT, manufacturing, smart cars, power grids, etc., but at the same time highlight the security and privacy issues in this age of big data. One of the serious side effects of the digital age and big data is the growing risk in terms of Trust, Security, and Privacy.
In this regard, Big Data is changing cybersecurity analytics by providing new tools and opportunities for leveraging large quantities of structured and unstructured data. The humongous scale of extraordinary scale, security, and privacy in big data faces many challenges, such as generative adversary networks, efficient encryption, and decryption algorithms, encrypted information retrieval, attribute-based encryption, attacks on availability, and reliability.
In this talk, we will discuss the Big Data system's security layered abstraction (Apache Hadoop stack). This talk involves several essential functions and frameworks, including Identity and access control of the Apache Hadoop clusters, data governance, integrity, confidentiality, and security auditing. Related research areas and associated concepts in data streaming IoT architectures, and cloud will be discussed as well.
Dr. Feras M. Awaysheh is a senior research fellow and academic lecturer at the Data Systems Group. He received his Ph.D. in Big Data and Cloud Computing from CiTIUS research center, the University of Santiago de Compostela (honors with distinction), Spain, in 2020. He holds an MSc. degree (with Hons.) in Computer Networks and Information Security from the New York Institute of Technology (NYiT) 2010 and a BSc. in Software Engineering from Al Balqa' Applied University, Jordan 2008. Dr. Awaysheh worked as a visiting research fellow in both Charles Darwin University, Australia, and the EPCC research center at Edinburgh University, the UK, in 2019 and 2020, respectively.
His research interest covers mainly Big Data (BD) deployment architectures and their applications. Dr. Awaysheh's interests include BD Engineer, i.e., modeling, designing, and optimizing platforms for BD frameworks across multiple deployment architectures and environments using containerization technology. His recent research interests focus on related technologies and include High-Performance Data Analytics, Data Streaming, IoT, and Edge/Fog models. Besides, the development of privacy-preserving and security frameworks for cloud-enabled solutions and the Apache Hadoop (Yarn) Ecosystem. He is a lecturer in Data Engineering, Big Data Management, and Advanced Database courses.