Keynote Speakers

Privacy-Enhanced Sustainable Use of Personal Health Data with Blockchain

Qun Jin
Professor and Dean of Graduate School of Human Sciences, Waseda University, Japan

Abstract
Wearable devices, sensors and digital traces make it possible to collect a variety of life log data in the course of daily activities, which is regarded to be of great value to healthcare and can shed light on aspects of lifestyle and health that were previously difficult to examine and measure. However, using this kind of personal health data may cause a critical privacy issue. On the other hand, recently blockchain gained the spotlight as a promising technology of benefit to mankind with advanced features such as decentralization and data security protection. In this talk, we focus on how to fully harness the advantage of blockchain technology to protect and enhance security and privacy while sharing and using personal health data for good. We present our vision and work on blockchain-empowered sustainable use and sharing of personal health data based on i-Blockchain: an individual-centric framework of blockchain for data security and privacy enhancement. Furthermore, we describe and discuss privacy-preserving personal analytics and individual modeling, extensively comparative analysis on health data and individualized visualization toward precision healthcare.

Short Bio
Qun Jin is a professor at the Networked Information Systems Laboratory, Department of Human Informatics and Cognitive Sciences, Faculty of Human Sciences, Waseda University, Japan. He is currently the Dean of Graduate School of Human Sciences, and the Deputy Dean for International Affairs, Faculty of Human Sciences. He has been extensively engaged in research works in the fields of computer science, information systems, and human informatics. His recent research interests cover human-centric ubiquitous computing, behavior and cognitive informatics, big data, personal analytics and individual modeling, intelligence computing, blockchain, cyber security, cyber-enabled applications in healthcare, and computing for well-being. He authored or co-authored several monographs and more than 300 refereed papers published in the world-renowned academic journals, including IEEE and ACM Transactions, and international conference proceedings, among which a few were granted best paper awards. He served as a general chair, program chair, and keynote speaker for numerous IEEE sponsored international conferences. He served as a guest editor in recent years for IEEE Transactions on Industrial Informatics (2019), IEEE/ACM Transactions on Computational Biology and Bioinformatics (2018), IEEE Transactions on Computational Social Systems (2018), IEEE Transactions on Emerging Topics in Computing (2017), IEEE MultiMedia (2017), and IEEE Cloud Computing (2015). He is a foreign member of the Engineering Academy of Japan (EAJ).

Cyber-Physical-Social Intelligence: System Design, Data Analytics, Security and Privacy

Laurence T. Yang
Department of Computer Science
St Francis Xavier University, Canada

The booming growth and rapid development in embedded systems, wireless communications, sensing techniques and emerging support for cloud computing and social networks have enabled researchers and practitioners to create a wide variety of Cyber-Physical-Social Systems (CPSS) that reason intelligently, act autonomously, and respond to the users¡¯ needs in a context and situation-aware manner.  The CPSS are the integration of computation, communication and control with the physical world, human knowledge and sociocultural elements. It is a novel emerging computing paradigm and has attracted wide concerns from both industry and academia in recent years.

Currently, CPSS are still in their infancy stage. Our first ongoing research is to study effective and efficient approaches for CPSS modeling and general system design automation methods, as well as methods analyzing and/or improving their power and energy, security, trust and reliability features.

Once the CPSS have been designed, they collect massive data (Volume) from the physical world by various physical perception devices (Variety) in structured/semi-structured/unstructured format and respond the users¡¯ requirements immediately (Velocity) and provide the proactive services (Veracity) for them in physical space or social space. These collected big data are normally high dimensional, redundant and noisy, and many beyond the processing capacity of the computer systems. Our second ongoing research is focused on the Data-as-a-Service framework, which includes data representation, dimensionality reduction, incremental and distributed processing (securely on cloud), deep learning, clustering, prediction and proactive services, aiming at representing and processing big data generated from CPSS, providing more valued smart services for human and refining the previously designed CPSS.

This talk will present our latest research on these two directions. Corresponding case studies in some applications such as smart home and traffics will be shown to demonstrate the feasibility and flexibility of the proposed system design methodology and analytic framework.

Short Bio:
Laurence T. Yang got his BE in Computer Science and Technology and BSc in Applied Physics both from Tsinghua University, China and Ph.D in Computer Science from University of Victoria, Canada. He is a professor and W.F. James Research Chair at St. Francis Xavier University, Canada. His research includes parallel and distributed computing, embedded and ubiquitous/pervasive computing, and big data. He has published around 400 international journal papers in the above areas, of which half on top IEEE/ACM Transactions and Journals, others mainly on Elsevier, Springer and Wiley Journals. In recent several years, 5 and 24 papers have been listed as top 0.1% and top 1% highly-cited ESI papers, respectively.

He has been involved actively act as a steering chair for 6+ IEEE international conferences. He served as the vice-chair of IEEE CS Technical Committee of Supercomputing Applications (2001-2004), the chair of IEEE CS Technical Committee of Scalable Computing (2008-2011). He was the vice-chair (2014) and the chair (2015) of IEEE Canada Atlantic Section. ?Now he is the chair of IEEE CS Technical Committee of Scalable Computing (2018-), the co-chair of IEEE SMC Technical Committee on Cybermatics (2016-) and the vice-chair of IEEE CIS Technical Committee on Smart World (2016-2018).

In addition, he was the editors-in-chief of several international journals. Now he is serving as an editor for many international journals (such as IEEE Systems Journal, IEEE Access, Future Generation of Computer Systems (Elsevier), Information Sciences (Elsevier), Information Fusion (Elsevier), Big Data Research (Elsevier), etc). He has been acting as an author/co-author or an editor/co-editor of more than 25 books from well-known publishers. He has been invited to give around 40 keynote talks at various international conferences and symposia.

His recent honours and awards include Fellow of Institute of Electrical and Electronics Engineers (2020), IEEE TCBD Best Journal Paper Award (2019), Clarivate Analytics Highly Cited Researcher (2019), Fellow of Engineering Institute of Canada (2019), AMiner Most Influential Scholar Award for Internet of Things (2018), IEEE TCCPS Distinguished Leadership Award on Cyber-Physical Systems (2018), IEEE SCSTC Life-Career Achievement Award on Smart Computing (2018), Fellow of Canadian Academy of Engineering (2017), IEEE System Journal Best Paper Award (2017), IEEE TCSC Award for Excellence in Scalable Computing (2017), and the PROSE Award on Engineering and Technology (2010).

Differential Privacy for Smart Metering

Prof. Jinjun Chen
Swinburne University of Technology, Australia

Abstract:
Smart Meters continuously report user data to data centres. By analyzing the data collected, we can reason out user privacy such as living style and utility usage patterns. How to protect user privacy in smart metering system comes to the picture and calls for effective solutions. Especially, we need to consider data utility when preserving user privacy. In this talk, we will present an approach based differential privacy to address this challenge.

Short Bio:
Dr Jinjun Chen is a Professor from Swinburne University of Technology, Australia. He is Deputy Director of Swinburne Data Science Research Institute. He holds a PhD in Information Technology from Swinburne University of Technology, Australia. His research interests include data privacy and security, cloud computing, data systems and related various research topics. His research results have been published in more than 160 papers in international journals and conferences, including various IEEE/ACM Transactions. He received various awards such as UTS Vice-Chancellor's Awards for Research Excellence Highly Commended (2014), UTS Vice-Chancellor's Awards for Research Excellence Finalist (2013), Swinburne Vice-Chancellor¡¯s Research Award (ECR) (2008), IEEE Computer Society Outstanding Leadership Award (2008-2009) and (2010-2011). He is an Associate Editor for ACM Computing Surveys, IEEE Transactions on Knowledge and Data Engineering as well as other journals such as Journal of Computer and System Sciences.

1 International Conference on Information Science and Control Engineering