Professor Wanlei Zhou
Alfred Deakin Professor, Chair of Information Technology,
School of Information Technology,
Deakin University, Melbourne, Australia
https://www.deakin.edu.au/~wanlei
Title: Privacy in Location Based Services
Abstract
In this talk we first systematically present the current research status of the location privacy issue, including the location privacy definition, the attacks and adversaries, the location privacy preserving mechanisms, the location privacy metrics, and the current status of location based applications. Then we will present two application cases related to location privacy. The first applicaiton case is to enhace privacy of location-based services (LBS) in wireless vehicular networks, where we develop an LBS privacy-enhancing scheme that is dedicated to the vehicular environment by exploring the unique features of queries from in-vehicle users. The second application case is to deal with the trajectory privacy preserving in mobile crowd sensing (MCS), where we develop a location privacy preserving framework based on economic models for MCS applications. The presentation will be mainly based on a survey paper we are currently working on (Location Privacy and the Applications: A Systematic Study) and the following two published papers:
1. Bo Liu, Wanlei Zhou, Tianqing Zhu, Longxiang Gao, Tom H. Luan, and Haibo Zhou, "Silence is Golden: Enhancing Privacy of Location-Based Services by Content Broadcasting and Active Caching in Wireless Vehicular Networks", IEEE Transactions on Vehicular Technology. VOL. 65, NO. 12, pp. 9942-9953, December 2016.
2. Bo Liu, Wanlei Zhou, Tianqing Zhu, Haibo Zhou, Xiaodong Lin, "Invisible Hand: Economic Model based Trajectory Privacy Preserving Schemes in Mobile Crowd Sensing Applications". IEEE Transactions on Vehicular Technology. VOL. 66, NO. 5, pp.4410-4423, MAY 2017.
Bio
Professor Wanlei Zhou received the B.Eng and M.Eng degrees from Harbin Institute of Technology, Harbin, China in 1982 and 1984, respectively, and the PhD degree from The Australian National University, Canberra, Australia, in 1991, all in Computer Science and Engineering. He also received a higher Doctorate degree, the DSc degree, from Deakin University in 2002. He is currently the Alfred Deakin Professor (the highest honour the University can bestow on a member of academic staff), Chair of Information Technology, and Associate Dean (International Research Engagement) of Faculty of Science, Engineering and Built Environment, Deakin University. Professor Zhou has been the Head of School of Information Technology twice (Jan 2002-Apr 2006 and Jan 2009-Jan 2015) and Associate Dean of Faculty of Science and Technology in Deakin University (May 2006-Dec 2008). Before joining Deakin University, Professor Zhou served as a lecturer in University of Electronic Science and Technology of China, a system programmer in HP at Massachusetts, USA; a lecturer in Monash University, Melbourne, Australia; and a lecturer in National University of Singapore, Singapore. His research interests include distributed systems, network security, bioinformatics, and e-learning. Professor Zhou has published more than 300 papers in refereed international journals and refereed international conferences proceedings. He has also chaired many international conferences. Prof Zhou is a Senior Member of the IEEE.
Professor Jie Lu
University of Technology Sydney, Australia
https://www.uts.edu.au/staff/jie.lu
Title: Fuzzy Transfer Learning for Prediction
Abstract
This presentation highlights the value of fuzzy transfer learning methods and related algorithms for handling complex prediction problems in rapidly-changing data distribution and data-shortage situations. It provides a framework for utilizing previously-acquired knowledge to predict new but similar problems quickly and effectively by using fuzzy set techniques. It systematically presents developments in fuzzy set-based transfer learning methods for prediction, including fuzzy transfer learning-based prediction framework, fuzzy domain adaptation, fuzzy cross-domain adaptation, and in particular, cross-domain adaptive fuzzy inference system, and their respective applications in prediction and decision support. This presentation demonstrates the successful use of fuzzy techniques in facilitating the incorporation of approximation and expressiveness of data uncertainties within knowledge transfer, machine learning and data-driven decision support systems.
Bio
Distinguished Professor Jie Lu is an internationally established scientist in the areas of decision support systems, fuzzy transfer learning, concept drift, recommender systems, prediction and early warning systems. She is the Associate Dean in Research Excellence in the Faculty of Engineering and Information Technology at the University of Technology Sydney. She is also the Director of the Centre for Artificial Intelligence (CAI). She has published six research books and more than 400 papers in refereed journals and conference proceedings. She has won eight Australian Research Council (ARC) discovery grants and 10 other research grants in the last 15 years. She serves as Editor-In-Chief for Knowledge-Based Systems (Elsevier) and as Editor-In-Chief for International Journal on Computational Intelligence Systems (Atlantis), has delivered 15 keynote speeches at international conferences, and has chaired 10 international conferences. She is an ARC panel member (2016-2018).
Professor Joe Dong
School of Electrical Engineering and Telecommunications, UNSW, Sydney. IEEE Fellow
https://www.engineering.unsw.edu.au/electrical-engineering/prof-joe-dong
Title: Towards Reliable, Secure and Efficient Energy Supply with Large Renewable Energy at Affordable Price
Abstract
In order to achieve the 33,000GWh renewable energy generation target by 2020, there have been large number of renewable, especially, solar energy development happening in Australia. At the same time, some of the existing coal fired power plants are reaching their life towards decommissioning. It has posed major challanges on the reliability and security of the energy system. The key issues related to the seucrity of energy systems and potential solution technologies will be presented in this talk.
Bio
Professor Joe Dong is with the School of Electrical Engineering and Telecommunications, UNSW, Sydney. His immediate role was Head of School of Electrical and Information Engineering in the University of Sydney, and Ausgrid Chair and Director of Ausgrid Centre of Excellence for Intelligent Electricity Networks (CIEN) at the University of Newcastle, Australia. He has also worked for Hong Kong Polytechnic University and as system planning manager with Transend Networks (now TASNetworks), Australia. Professor Dong's research interest includes power system planning and stability, smart grid/micro-grid, load modeling, renewable energy grid connection, electricity market, data mining, big data analytics, artificial intelligence and computational methods. He has served as editor for a number of international top journals such IEEE Transactions on Smart Grid, IEEE PES Letters, IEEE Transactions on Sustainable Energy, IET Renewable Power Generation, and Journal of Modern Power Systems and Clean Energy. He is an international Advisor for the lead Chinese journal of Automation of Electric Power Systems and a guest editor for Southern Power System Technology under China Southern Power Grid. He also serves as guest editor for International Journal of Systems Science. Prof Dong is a Fellow of IEEE.
Professor Yongsheng Gao
Griffith University, Australia
Title: Intermediate- and High-Level Structure Matching and Its Application in Biometrics
Bio
Prof. Yongsheng Gao is full professor at Griffith School of Engineering, Griffith University, Australia. Professor Gao has active research interests in the fields of Biometric Technology, Multimedia Data Retrieval Systems, Pattern Recognition, Computer Vision and Biomedical Engineering.
Dr. Shui Yu
Deakin University, Australia
https://www.nsp.org.au/syu/
Title: Networking for Big Data: Challenges and Opportunities
Abstract
Big Data is one of the hottest topics in our communities, and networking is an indispensable corner stone for the fancy big data applications. As a result, there is an emerging research branch, Networking for Big Data (NBD), in networking and communication fields. In this talk, we will firstly overview the current landscape of this energetic area, and then present the unprecedented challenges in this new domain, and finally discuss the current research directions in the main topics in networking for big data. We humbly hope this talk will shed light for forthcoming researchers to further explore the uncharted part of this promising land.
Bio
Shui Yu is currently a Senior Lecturer of School of Information Technology, Deakin University. He is a member of Deakin University Academic Board (2015-2016), a Senior Member of IEEE, and a member of AAAS and ACM, the Vice Chair of Technical Committee on Big Data Processing, Analytics, and Networking of IEEE Communication Society, and a member of IEEE Big Data Standardization Committee.
Dr Yu’s research interest includes Security and Privacy in Networking, Big Data, and Cyberspace, and mathematical modelling. He has published two monographs and edited two books, more than 150 technical papers, including top journals and top conferences, such as IEEE TPDS, IEEE TC, IEEE TIFS, IEEE TMC, IEEE TKDE, IEEE TETC, and IEEE INFOCOM. Dr Yu initiated the research field of networking for big data in 2013. His h-index is 25. Dr Yu actively serves his research communities in various roles. He is currently serving the editorial boards of IEEE Communications Surveys and Tutorials, IEEE Access, IEEE Journal of Internet of Things, IEEE Communications Magazine, and a number of other international journals. He has served more than 70 international conferences as a member of organizing committee, such as publication chair for IEEE Globecom 2015 and 2017, IEEE INFOCOM 2016 and 2017, TPC co-chair for IEEE BigDataService 2015, IEEE ATNAC 2014, IEEE ITNAC 2015; Executive general chair for ACSW2017.
