Cyber security (privacy-preserving distributed edge computing)
Campus: Mt Helen
Discipline: Information technology
Research field: Cyber security
Key words: Cyber security, SCADA security, IoT security, edge computing, multimedia security
Supervisor(s): Alireza Jolfaei and Iqbal Gondal
Brief Supervisor Bio:
Dr. Alireza Jolfaei Received the PhD degree from Griffith University, Gold Coast, QLD, Australia. He is currently a lecturer of networking and security in the School of Engineering and Information Technology at Federation University Australia. Previously, he was an Assistant Professor of Computer Science in the Department of Computer and Information Sciences at Temple University, Philadelphia, Pennsylvania, USA. His educational background and research activities extend across substantive areas in applied mathematical sciences, computer science/engineering, and electrical engineering. His current research areas include cryptology, cyber physical systems security, SCADA security, and network security. He has authored over 30 papers on topics related to cyber security in refereed journals and conference proceedings of international importance. He received the prestigious IEEE Australian council award for his research paper published in the IEEE Transactions on Information Forensics and Security. He has received multiple awards for Academic Excellence, University Contribution, and Inclusion and Diversity Support. He is currently serving as the Chair of Computational Intelligence Society in IEEE Victoria Section, and previously, he served as the Chair of Professional and Career Activities in IEEE Queensland Section. He is a member of the Internet Commerce Security Laboratory (ICSL) and the Centre for Informatics and Applied Optimization (CIAO) at Federation University Australia.
Project description: Privacy-preserving distributed edge computing
The highly distributed nature of IoT along with the sheer scale of mobile and ubiquitous computing poses significant challenges providing timely processing and exchange of large amounts of data and enabling security and privacy when collecting and processing data. Currently, the implementation of analytics extraction models largely exists within vast cloud infrastructures. However, given that edge computing is pushing the network computation towards the data-source, the IoT or mobile device is a promising approach for simultaneously addressing security, privacy and efficacy challenges. There are however additional challenges in modelling and design to effectively implement such privacy-preserving edge computing at scale for many real-world scenarios. This research will focus on exploring approaches to optimise the privacy and utility of analytics extraction at the end-user or the networks edge addressing adequate systems design to offer privacy/security to users, personalised AI-based products with low network delay and large-scale data-collection and exchange.