Keynote Speech 1:
Preserving privacy in the digital age: Differential privacy and its applications


Abstract:

Over the past two decades, digital information collected by corporations, organisations and governments has created huge amount of datasets, and the speed of such data collection has increased exponentially over the last a few years because of the pervasiveness of computing devices. However, most of the collected datasets are personally related and contain private or sensitive information. Even though curators can apply several simple anonymization techniques, there is still a high probability that the sensitive information of individuals will be disclosed. Privacy-preserving has therefore become an urgent issue that needs to be addressed in the digital age.
Differential privacy is one of the most prevalent privacy models as it provides a rigorous and provable privacy notion that can be implemented in various research areas. In this presentation, we will start with privacy breaches and privacy models, and introduce the basic concept of differential privacy. We then will forcus on the applications of differential privacy in various senarios in which we have been working on, including Location privacy, Recommender systems, Tagging systems, and Correlated datasets. We will then finalise the talk by outlining the privacy challenges in the era of big data.

Professor Wanlei Zhou

Professor Wanlei Zhou

Head, School of Software, University of Technology Sydney, Australia

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 DSc degree (a higher Doctorate degree) from Deakin University in 2002. He is currently the Head of School of Software in University of Twechnology Sydney (UTS). Before joining UTS, Professor Zhou held the positions of Alfred Deakin Professor, 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). Professor Zhou also 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 security and privacy, bioinformatics, and e-learning. Professor Zhou has published more than 400 papers in refereed international journals and refereed international conferences proceedings, including many articles in IEEE transactions and journals.

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 Dr Tianqing Zhu

Dr Tianqing Zhu

School of Software, University of Technology Sydney, Australia

Dr Tianqing Zhu received her BEng and MEng degrees from Wuhan University, China, in 2000 and 2004, respectively, and a PhD degree in Computer Science from Deakin University, Australia, in 2014. Dr Tianqing Zhu is currently a Senior Lecturer in the School of Software in University of Technology Sydney, Australia (UTS). Before joining UTS, she served as a lecturer in School of Information Technology, Deakin University, Melbourne Australia from 2014 to 2018 and a lecturer in Wuhan Polytechnic University, China from 2004 to 2011. Her research interests include privacy preserving, data mining and network security. She has won the best student paper award in PAKDD 2014 and was invited to give tutorials on privacy preserving in a number of international conferences including PAKDD 2015, SociaSec 2015, GPC 2017, etc.

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Plenary Speech:
Evolutionary Optimization in Algebraic Time Series Prediction � Problems and Applications


Abstract:


Time series forecasting is a challenging problem in many fields of science and engineering. In general, the main objective of any predictor is to build a model of the process and then use this model on the last values of the time series to extrapolate past behaviour into the future. A class of novel short-term time series prediction algorithms will be presented in this talk. The proposed predictors with internal, external and mixed smoothing employ target functions which help to achieve the necessary balance between the roughness of the algebraic prediction and the smoothness of the prediction based on moving averaging. Such balancing results into a difficult optimization problem which is solved using machine learning techniques.
 Prof Minvydas Ragulskis

Professor Prof Minvydas Ragulskis

Professor at Department of Mathematical Modelling, Kaunas University of Technology, Lithuania

Minvydas Ragulskis is a full professor at Department of Mathematical Modelling, Kaunas University of Technology, Lithuania. He is a Fellow of Lithuanian National Academy of Sciences and serves as an invited expert at various National and International Committees including Research Executive Agency, Brussels. M.Ragulskis takes the position of the High-end Foreign Expert of Jiangsu Province at Hohai University and the position of Honorary Professor at Jinan University, P.R. China. He has published over 100 papers in International Journals and has been invited as a Key-note speaker at a number of International Conferences.

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Keynote Speech 2:
TBA


Abstract:


Coming soon ....
 Dr Oliver Obst

Dr Oliver Obst

Associate Professor In Data Science, Deans Unit School Of Computing, Engineering & Math, Western Sydney University, Australia

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