IEEE DSC 2017 - IEEE International Conference on Data Science in Cyberspace

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Invited Talk

Data-Driven Cyber Security

Speaker: Yang Xiang

Abstract: Today we have evidenced massive cyber attacks having hit millions of people in more than 150 countries with billions of dollars lose. Cyber security has become one of the top priorities in the research and development agenda globally.
In the big data era, we face a diversity of datasets from a huge number of sources in different domains. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale, and density.
It has been widely recognized that the power of knowledge from multiple disparate (but potentially connected) datasets is paramount. For example, collecting multiple sources of information from online social networks has become common exercise to deal with social security problems.
Big data analytics are some of the most effective defenses against cyber intrusions. Better, faster, actionable security information reduces the critical time from detection to remediation, enabling cyber warfare specialists to proactively defend and protect cyberspace.
New methods and tools, consequently, must follow up in order to adapt to this emerging security paradigm. In this talk, we will discuss the concept of Data-Driven Cyber Security and how big data analytics can be used to address the security and privacy problems in cyberspace.

Biography: Professor Yang Xiang received his PhD in Computer Science from Deakin University, Australia. He is the Director of Centre for Cyber Security Research at Deakin University. His research interests include network and system security, distributed systems, and data analytics. He has published more than 200 research papers in international journals and conferences, such as IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Information Security and Forensics, and IEEE Journal on Selected Areas in Communications. He serves as the Associate Editor of IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, Security and Communication Networks (Wiley), and the Editor of Journal of Network and Computer Applications (Elsevier). He is a Senior Member of the IEEE.

Graph Encryption for Exact Shortest Distance Queries

Speaker: Qian Wang

Abstract: In the era of big data, graph databases have become increasingly important for NoSQL technologies, and many systems can be modeled as graphs for semantic queries. Meanwhile, with the advent of cloud computing, data owners are highly motivated to outsource and store their massive potentially-sensitive graph data on remote untrusted servers in an encrypted form, expecting to retain the ability to query over the encrypted graphs.
In this talk, we will first provide a brief introduction to searchable symmetric encryption (SSE) designs, which encrypt search structures for retrieving data files. Then we tackle the challenge of designing a Secure Graph DataBase encryption scheme (SecGDB) to encrypt graph structures and enforce private graph queries over the encrypted graph database. We prove that our construction is adaptively semantically-secure and finally implement and evaluate it on various representative real-world datasets, showing that our design is practically efficient in terms of both storage and computation.

Biography: Qian Wang is a Professor with the School of Computer Science, Wuhan University. He received the B.S. degree from Wuhan University, China, in 2003, the M.S. degree from Shanghai Institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences, China, in 2006, and the Ph.D. degree from Illinois Institute of Technology, USA, in 2012, all in Electrical Engineering. His research interests include wireless network security and privacy, cloud computing security, big data security and privacy, and applied cryptography. Qian is an expert under National “1000 Young Talents Program” of China. He is a recipient of IEEE Asia-Pacific Outstanding Young Researcher Award 2016. He is also a co-recipient of several Best Paper Awards from IEEE ICNP’11, WAIM’14, and IEEE TrustCom’16 etc. He serves as an Associate Editor for IEEE Transactions on Information Forensics and Security (T-IFS). He is a Member of the IEEE and a Member of the ACM.

Towards Trustworthy and Interactive Queries on Big Spatial Data

Speaker: Jianliang Xu

Abstract: The ubiquity of location-based services (LBS) makes spatial data readily available for search, analysis and retrieval. However, the overwhelming data volume and variety pose new research challenges as well as new opportunities for query processing on big spatial data. In particular, the service provider can be untrustworthy or compromised, thereby raising security threats on data integrity. To enhance system usability and user experience, it is important to provide interactive and verifiable responses to queries. In this talk, we will present several of our recent efforts that are aimed to improve the functionality, usability, and performance of spatial query services on big data. We will also discuss some possible future research directions.

Biography: Jianliang Xu is a Professor in the Department of Computer Science, Hong Kong Baptist University (HKBU). He received his BEng degree from Zhejiang University and his PhD degree from Hong Kong University of Science and Technology. His current research interests include big data management, data security and privacy, and database systems. With an h-index of 40, he has published more than 150 technical papers in these areas, most of which appeared in leading journals and conferences including SIGMOD, PVLDB, ICDE, TODS, TKDE and VLDBJ. He has served as a program co-chair/vice chair for a number of major international conferences including IEEE ICDCS 2012, IEEE CPSNA 2015 and WAIM 2016. He was a recipient of HKBU President's Award for Outstanding Performance in Scholarly Work (2017). He is an Associate Editor of IEEE Transactions on Knowledge and Data Engineering (TKDE) and Proceedings of the VLDB Endowment (PVLDB 2018).

Software Defined Anything (SDX)

Speaker: Yan Zhang

Abstract: The main principle of Software Defined Networks (SDN) is to decouple the control plane and the forwarding plane in Internet. This principle may not be limited to the design and operation of the Internet. It is envisioned that such principle can be generalized to separate the control plane from the data plane in all networks/systems/applications. In this context, we may expect software defined any systems in Internet of Things, also known as Software Defined Anything (SDX). In this talk, we will first present the key concepts and architectures related to SDX. Then, we will present our recent studies related to software defined vehicular networks, software defined smart grid, and software defined wireless networks.

Biography: Prof. Yan Zhang is Full Professor at the Department of Informatics, University of Oslo, Norway. He received a PhD degree in School of Electrical & Electronics Engineering, Nanyang Technological University, Singapore.
He serves as an Associate Technical Editor of IEEE Communications Magazine, an Editor of IEEE Transactions on Green Communications and Networking, an Editor of IEEE Communications Surveys & Tutorials, an Editor of IEEE Internet of Things Journal, and an Associate Editor of IEEE Access. He serves as chair positions in a number of conferences, including IEEE GLOBECOM 2017, IEEE VTC-Spring 2017, IEEE PIMRC 2016, IEEE CloudCom 2016, IEEE ICCC 2016, IEEE CCNC 2016, IEEE SmartGridComm 2015, and IEEE CloudCom 2015. He serves as TPC member for numerous international conferences including IEEE INFOCOM, IEEE ICC, and IEEE GLOBECOM. He has seven ESI “Highly Cited Papers”. He is IEEE VTS (Vehicular Technology Society) Distinguished Lecturer. He serves as IEEE TCGCC Vice Chair. He is also a senior member of IEEE, IEEE ComSoc, IEEE PES, and IEEE VT society. He is a Fellow of IET. His current research interests include: next-generation wireless networks leading to 5G, reliable and secure cyber-physical systems (e.g., smart grid, transport, and healthcare).

Data Driven Large-Scale Fuzzy Cognitive Map Learning based on Evolutionary Algorithms

Speaker: Jing Liu

Abstract: Fuzzy cognitive maps (FCMs), a kind of effective tools for creating models for complex systems, are cognition fuzzy influence graphs, which are based on fuzzy logic and neural networks. FCMs have several advantages in terms of abstraction, flexibility, adaptability, and fuzzy reasoning than traditional modeling techniques such as expert systems and neural networks. Therefore, they have been proposed and applied in a variety of applications such as medical diagnosis, time series analysis, pattern recognition, and modeling of software development project. Many automated learning algorithms have been proposed to learn FCMs from data. This talk focuses on introducing single objective and multi-objective evolutionary algorithm-based FCM learning methods, which can automatically learn large-scale FCMs from time series data.

Biography: Jing Liu received the B.S. degree in computer science and technology and the Ph.D. degree in circuits and systems from Xidian University in 2000 and 2004, respectively. In 2005, she joined Xidian University as a lecturer, and was promoted to a full professor in 2009. From Apr. 2007 to Apr. 2008, she worked at The University of Queensland, Australia as a postdoctoral research fellow, and from Jul. 2009 to Jul. 2011, she worked at The University of New South Wales at the Australian Defence Force Academy as a research associate. Now, she is a full professor in the Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University. Her research interests include evolutionary computation, complex networks, fuzzy cognitive maps, multiagent systems, and data mining. She is the associate editor of IEEE Trans. Evolutionary Computation and the chair of Emerging Technologies Technical Committee in IEEE Computational Intelligence Society.

Knowledge Graph Inference Based on Representation Learning

Speaker: Bin Wang

Abstract: A Knowledge Graph (KG) is a directed graph, which represents entities as nodes, their relations as edges. KGs are very important in many applications such as information retrieval , question answering or content recommendation systems. However, most current KGs are extremlely sparse thus can only cover very few knowledge. KG inference is to predict the links between entities in KGs, which can be used to improve the knowledge coverage of KGs. This talk will first review some concepts and exsiting work about KG inference, then concentrate on representation learning based approaches, which have many advantages in both efficiency and effectiveness. Finally, the talk will introduce two of our approaches, which consider the space smoothness and logic rules repectively.

Biography: Bin Wang is a Professor of Institute of Information Engineering, Chinese Academy of Sciences. His research interests include information retrieval, natural language processing and social network analysis. Dr. Wang received his Ph.D. degree from Institute of Computing Technology, Chinese Academy of Sciences in 1999. He has published more than 150 research papers in academic journals and conferences including TKDE, SIGIR, CIKM, ACL, EMNLP, IJCAI, AAAI, etc. He served as PC member of SIGIR, CIKM, ACL and area PC co-chair of AIRS, NLPCC, CCL,CCKS. He also served as technical member of several academic associations or committees.