讲座一：Challenging issues on emerging wireless networks
讲座简介：In today’s digitally connected smart environments, billions of distributed heterogeneous devices are already being connected and communicated over dynamic wireless networks. The embedded big data if intelligently explored and exploited will lead to many valuable applications for individuals, business and industrial communities. The underlying communication technology rests on the software enabled service architecture (SaaS, PaaS, Iaas) that are used to support of business and e-Government policy decision makers. In this presentation, I shall first explain the design concept and goals, followed by discussion of deployment issues (architecture and emerging technologies) for 5G wireless sensor networks; Internet of Things (IoT) and big data value extractions via fusion of data through to knowledge for various application domains. Examples of ongoing research issues/challenges on infrastructural health monitoring and assessment metric, biosensor and wearable device for personalised health care, and social networks will also be discussed.
Secured Multiparty Computation - DAG: A General Model for Privacy-Preserving Data Mining
讲座简介：Data and Information security and privacy preserving are challenging issues arising from the widespread use of clouds as distributed computing and data storage. Efficient algorithms are needed in the form of secure multi-party computation (SMC) that allows parties to jointly compute a function over their inputs, while keeping every input confidential. It has been extensively applied in tasks with privacy requirements, such as privacy-preserving data mining (PPDM), to learn task output and at the same time protect input data privacy. However, existing SMC-based solutions are ad-hoc – they are proposed for specific applications, and thus cannot be applied to other applications directly. To address this issue, we propose a privacy model DAG (Directed Acyclic Graph) that consists of a set of fundamental secure operators (e.g., +, -, _, /, and power). Our model is general – its operators, if pipelined together, can implement various functions, even complicated ones like N?ve Bayes classifier. It is also extendable – new secure operators can be defined to expand the functions the model supports. For case study, we have applied our DAG model to two data mining tasks: kernel regression and Na¨?ve Bayes. Experimental results show that DAG generates outputs that are almost the same as those by non-private setting, where multiple parties simply disclose their data. The experimental results also show that our DAG model runs in acceptable time, e.g., in kernel regression, when training data size is 683,093, one prediction in non-private setting takes 5.93 sec, and that by our DAG model takes 12.38 sec.
PhD (University of New Castle, 1992, Australia), is an associate professor with Faculty of IT, Monash University in Melbourne of Australia. He is a multi- and inter-disciplinary researchers across adaptive signal processing and control system, computational intelligence, economic and finance, business information systems, and process mining with privacy preserving and information security cryptography systems. His current research focus on challenging issues (i.e. e-health, smart grid, privacy preserving in data, security analytics, and big data fusion) in the context of smart city. He has published approximately 200 articles in IEEE Transactions on Knowledge and Data Engineering, IEEE transactions on Signal Processing; IEEE Selected Areas in Communications; European Journal of Operational Research, Neuro-computing, Soft-computing, IEEE Privacy and Security Magazine, Accounting and Finance Journal); IEEE flagship and ACM international conference proceedings; and also in Springer LNCS & LNAI volumes of book chapters. Lee has served as invited general chairs, technical committee chairs for many IEEE and ACM international conferences. He has supervised 15 PhDs from beginning to the award of degree in the fields of data mining, adaptive signal processing with applications to healthcare, business, science and engineering problem formulation and innovative solutions development.