ABSTRACT:Vehicle platooning has been regarded as a promising intelligent transportation system technology for achieving cooperative automated driving systems and automated highway systems due to its promising benefits, including improved road safety, highway capacity and traffic congestion relief, and reduced fuel consumption. Two critical challenges of accomplishing automated vehicle platoons are: 1) to deal with the intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources; and 2) to tackle the malicious cyber-attacks on the vehicle-to-vehicle communication channels.

The essentials of evolutionary platooning control technologies are first introduced for connected automated vehicles. After a brief historical background of connected automated vehicles and vehicle platooning, several key issues in the design and implementation of an automated vehicle platooning control system are elaborated. An emphasis is then placed on two emerging platooning control techniques: resource-efficient vehicle platooning and secure vehicle platooning. Furthermore, simulation and validation results under these two control techniques are presented. Finally, some challenging issues and concluding remarks are drawn.

BIO:Professor Han is Pro Vice-Chancellor (Research Quality) and a Distinguished Professor at Swinburne University of Technology, Melbourne, Australia. He held various academic and management positions at Griffith University and Central Queensland University, Australia. His research interests include networked control systems, multi-agent systems, time-delay systems, smart grids, unmanned surface vehicles, and neural networks.

Professor Han was awarded The 2021 Norbert Wiener Award (the Highest Award in systems science and engineering, and cybernetics) and The 2021 M. A. Sargent Medal (the Highest Award of the Electrical College Board of Engineers Australia). He was the recipient of The 2021 IEEE/CAA Journal of Automatica Sinica Norbert Wiener Review Award, The 2020 IEEE Systems, Man, and Cybernetics (SMC) Society Andrew P. Sage Best Transactions Paper Award, The 2020 IEEE Transactions on Industrial Informatics Outstanding Paper Award, and The 2019 IEEE SMC Society Andrew P. Sage Best Transactions Paper Award.

Professor Han is a Member of the Academia Europaea (The Academy of Europe). He is a Fellow of The Institute of Electrical and Electronics Engineers (IEEE), a Fellow of The International Federation of Automatic Control (IFAC), and a Fellow of The Institution of Engineers Australia (IEAust). He is a Highly Cited Researcher in both Engineering and Computer Science (Clarivate Analytics). He has served as an AdCom Member of IEEE Industrial Electronics Society (IES), a Member of IEEE IES Fellows Committee, a Member of IEEE IES Publications Committee, and Chair of IEEE IES Technical Committee on Networked Control Systems. Currently, he is Co-Editor-in-Chief of IEEE Transactions on Industrial Informatics, Deputy Editor-in-Chief of IEEE/CAA JOURNAL OF AUTOMATICA SINICA, and Co-Editor of Australian Journal of Electrical and Electronic Engineering.

ABSTRACT:Increasing privacy and security concerns in intelligence-native 6G networks require quantum key distribution (QKD) networks. In QKD networks, edge devices connected via quantum channels can efficiently encrypt information from the source, and securely transmit the encrypted information to the destination. In this presentation, we first give a gentle introduction to quantum computing. The basic concepts and fundamentals about QKD networks are presented. Then, we discuss the resource allocation issues in QKD networks. We present a use case of stochastic QKD network resource allocation. The use case to demonstrate a stochastic optimization considering QKD service providers offering QKD services with different options to the edge devices. The objective of the optimization is to reduce the cost for the edge devices in provisioning secret-key rates from the QKD services given uncertain demands from the applications, e.g., semantic information transfer. The benefits of the proposed QDK resource allocation are validated through numerical studies. Finally, we discuss future research directions toward optimizing quantum computing and QKD resource management.

BIO:Dusit Niyato is currently a professor in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. Currently, Dusit is serving as editor-in-chief of IEEE Communications Surveys and Tutorials, an area editor of IEEE Transactions on Vehicular Technology, an editor of IEEE Transactions on Wireless Communications, an associate editor of IEEE Internet of Things Journal, IEEE Transactions on Mobile Computing, IEEE Wireless Communications, IEEE Network, and ACM Computing Surveys. He was a guest editor of IEEE Journal on Selected Areas on Communications. He was a Distinguished Lecturer of the IEEE Communications Society for 2016-2017. He was named the 2017-2021 highly cited researcher in computer science. He is a Fellow of IEEE.

ABSTRACT:This talk will provide an overview on the design and implementation of a system for secure access control, search, and computation of encrypted data in the cloud for enterprise users. The system is designed following the “zero trust” paradigm to protect data security and privacy even if cloud storage servers or user accounts are compromised. This is achieved using end-to-end (E2E) encryption in which encryption and decryption operations only take place at client devices. However, encryption must not hinder access, search and even computation of data by authorized users. There are numerous academic publications in this area and the choice of which cryptographic techniques to use could have significant impact on the system’s scalability, efficiency, and usability. We will share our experience in the design of the system architecture and selection of cryptographic techniques with a consideration to balance security, performance, and usability.

BIO:Robert Deng is AXA Chair Professor of Cybersecurity, Director of the Secure Mobile Centre, and Deputy Dean for Faculty & Research, School of Computing and Information Systems, Singapore Management University (SMU). His research interests are in the areas of data security and privacy, network security, and applied cryptography. He received the Outstanding University Researcher Award from National University of Singapore, Lee Kuan Yew Fellowship for Research Excellence from SMU, and Asia-Pacific Information Security Leadership Achievements Community Service Star from International Information Systems Security Certification Consortium. He serves/served on the editorial boards of ACM Transactions on Privacy and Security, IEEE Security & Privacy, IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Information Forensics and Security, Journal of Computer Science and Technology, and Steering Committee Chair of the ACM Asia Conference on Computer and Communications Security. He is a Fellow of IEEE and Fellow of Academy of Engineering Singapore.

ABSTRACT:Millimeter-wave (mmWave) sensing has shown a broad application prospect in the fields of unmanned driving and human body monitoring, due to its high resolution and long-distance sensing capability. This talk will introduce two state-of-the-art research in IoT security including speech privacy leakage and anti-counterfeit face recognition, based on the fine-grained perception of mmWave. These works expand the conventional sensing dimension and realizes the cross-modality transformation from wireless modality to speech and image modalities. Specifically, regarding speech privacy leakage, we investigate the remote speech recovery for mobile terminals by using earphone vibration perceived by wireless signal. Our work overcomes low signal-to-noise ratios problem originating from the light volume of mobile terminals and distortion problem caused by motion interference. Regarding anti-counterfeit face recognition, our work achieves non-visual imaging leveraging mmWave, and breaks through the limitations of traditional wireless sensing, i.e., extensive training data and black-box training. The proposed mmWave-based imaging system provides similar imaging effects close to visual imaging, which can be used in face recognition, even under occlusion and counterfeit attack.

BIO:Kui Ren is a Professor and the Associate Dean of the College of Computer Science and Technology, Zhejiang University, where he also directs the Institute of Cyber Science and Technology. Before that, he was the SUNY Empire Innovation Professor of The State University of New York at Buffalo. His H-index is 86 and his total publication citation exceeds 41000 according to Google Scholar. His current research interests include data security, the IoT security, AI security, and privacy. He has published extensively in peer-reviewed journals and conferences and received the Test-of-Time Paper Award from IEEE INFOCOM and many Best Paper Awards from IEEE and ACM, including MobiSys 2020, Globecom 2019, ASIACCS 2018, and ICDCS 2017. He received the NSF CAREER Award in 2011, the Sigma Xi Research Excellence Award in 2012, the IEEE CISTC Technical Recognition Award in 2017, the SUNY Chancellor’s Research Excellence Award in 2017, and the Guohua Distinguished Scholar Award from ZJU in 2020. Kui is a Fellow of ACM, a Fellow of IEEE, and a Clarivate Highly-Cited Researcher. He is a frequent reviewer for funding agencies internationally and serves on the editorial boards of many IEEE and ACM journals. He also serves as the Chair for SIGSAC of ACM China.

ABSTRACT:AI security has been a hot research area recently. In this talk, we briefly review some results on AI security. In particular, we talk of data privacy, adversarial examples, and backdoors. While our review is by no means comprehensive, it provides a quick summary of some research efforts that interest us.

BIO:Sheng Zhong received his BS and MS from Nanjing University, and PhD from Yale University. Now he works at Nanjing University, as Professor of Computer Science and Dean of School of Software Engineering. He is interested in security, privacy, and economic incentives.

ABSTRACT:Deep Neural Networks (DNNs) are well-known to be vulnerable to Adversarial Examples (AEs). A large amount of efforts have been spent to launch and heat the arms race between the attackers and defenders. Recently, advanced gradient-based attack techniques were proposed, which have defeated a considerable number of existing defense methods. Up to today, there are still no satisfactory solutions that can effectively and efficiently defend against those attacks. In this talk, we make a steady step towards mitigating those advanced gradient-based attacks with two major contributions. First, we perform an in-depth analysis about the root causes of those attacks, and propose four properties that can break the fundamental assumptions of those attacks. Second, we identify a set of operations that can meet those properties. By integrating these operations, we design two preprocessing functions that can invalidate these powerful attacks. Extensive evaluations indicate that our solutions can effectively mitigate all existing standard and advanced attack techniques.

BIO:Meikang Qiu received the BE and ME degrees from Shanghai Jiao Tong University and received Ph.D. degree of Computer Science from University of Texas at Dallas. Currently, He is a full professor and director of AI enhanced Cyber Security Lab of Dakota State University. He is an ACM Distinguished Member. He is also the Highly Cited Researcher in 2021 from Web of Science and IEEE Distinguished Visitor in 2021-2022. He is the Chair of IEEE Smart Computing Technical Committee. Till now his Google scholar citation is 19500+ and H-index 95. He is ranked within the top 1000 scientists in the world. He has won Navy Summer Faculty Award in 2012 and Air Force Summer Faculty Award in 2009. His research is supported by US government such as NSF, NSA, Air Force, Navy and companies such as GE, Nokia, TCL, and Cavium.

His research interests include Cyber Security, Big Data Analysis, Cloud Computing, Smarting Computing, Intelligent Data, Embedded systems, etc. A lot of novel results have been produced and most of them have already been reported to research community through high-quality journal and conference papers. He has published 20+ books, 600+ peer-reviewed journal and conference papers (including 300+ journal articles, 300+ conference papers, 100+ IEEE/ACM Transactions papers). His paper on Tele-health system has won IEEE System Journal 2018 Best Paper Award. His paper about data allocation for hybrid memory has been published in IEEE Transactions on Computers has been selected as IEEE TCSC 2016 Best Journal Paper and hot paper (1 in 1000 papers by Web of Science) in 2017. His paper published in IEEE Transactions on Computers about privacy protection for smart phones has been selected as a Highly Cited Paper in 2017-2020. He also won ACM Transactions on Design Automation of Electrical Systems (TODAES) 2011 Best Paper Award. He has won another 10+ Conference Best Paper Awards in recent years.

Currently he is/was an associate editor of 10+ international journals, including IEEE Transactions on Computers, IEEE Transactions on Cloud Computing, IEEE Transactions on Big Data, and IEEE Transactions on System, Man, and Cybernetics (A). He has served as leading guest editor for IEEE Transactions on Dependable and Secure Computing (TDSC), special issue on Social Network Security. He is the General Chair/Program Chair of a dozen of IEEE/ACM international conferences, such as IEEE TrustCom, IEEE BigDataSecurity, IEEE CSCloud, and IEEE HPCC.

ABSTRACT: The ubiquity of the Internet of Things brings with it new applications and infrastructural challenges. Edge computing is increasingly used to reduce latency and provide real time capabilities in this environment. However, IoTs and edge computing pose a unique challenge for security and privacy due to the sheer scale as well as the limited computational resources available. In this talk we discuss some of the research challenges and opportunities towards ensuring security and privacy in this environment.

BIO: Jaideep Vaidya is a Distinguished Professor of Computer Information Systems at Rutgers University. His primary research interests are in privacy, security, data mining, and data management. He has published over 200 papers in top-tier conferences and journals. His work has received numerous awards from the leading conferences in data mining, databases, security, informatics, and digital government. He is an IEEE Fellow and an ACM Distinguished Scientist. He is currently the editor-in-chief of the IEEE Transactions on Dependable and Secure Computing (TDSC).

ABSTRACT:Network-wide monitoring is important for many network functions, including network anomaly detection, network troubleshooting, and network service level agreement (SLA) tracking. However, there exists a fundamental dilemma: how to reduce the measurement overhead and the impact to the network while obtaining fine-grade accurate network-wide performance monitoring data? For a network consisting of n nodes, the cost of one time network-wide monitoring will be O(n2). Inspired by sparse representation technique, a breakthrough in signal processing, a novel network monitoring scheme, called sparse network monitoring, is proposed recently. It obtains the complete network-wide monitoring data by carefully selecting a subset of node pairs to take probe measurements and inferring the un-measurement data through sparse reconstruction algorithm. Under sparse network monitoring, only a small set of node pairs need to take probe measurements, thus the measurement overhead can be largely reduced. In this talk, I will illustrate the basic idea of sparse network monitoring, the challenge issues, and our recent progresses.

BIO:Kun Xie received the Ph.D. degree in computer application from Hunan University, Changsha, China, in 2007. She is currently a Professor with Hunan University. She has published over 100 articles in major journals and conference proceedings, including journals such as the IEEE/ACM TRANSACTIONS ON NETWORKING, the IEEE TRANSACTIONS ON MOBILE COMPUTING, the IEEE TRANSACTIONS ON COMPUTERS, the IEEE TRANSACTIONS ON Parallel and Distributed Systems, the IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, and the IEEE TRANSACTIONS ON SERVICES COMPUTING, and conferences, including SIGMOD, INFOCOM, ICDCS, SECON, DSN, and IWQoS. Her research interests include network measurement, network security, big data, and AI.