Prof. Bhavani Thuraisingham, PhD
Fellow of ACM, IEEE, AAAS, NAI, IMA
Erik Jonsson School of Engineering and Computer Science
The University of Texas at Dallas, USA
Title: Trustworthy Machine Learning and Its Applications in IoT Systems
The collection, storage, manipulation, analysis and retention of massive amounts of data have resulted in new technologies including big data analytics and data science. It is now possible to analyze massive amounts of data and extract useful nuggets. However, the collection and manipulation of this data has also resulted in serious security and privacy considerations. Various regulations are being proposed to handle big data so that the privacy of the individuals is not violated. Furthermore, the massive amounts of data being stored may also be vulnerable to cyber attacks. Furthermore, Artificial Intelligence Techniques including machine learning are being applied to analyze the massive amounts of data in every field such as healthcare, finance, retail and manufacturing.
Machine techniques are being integrated to solve many of the security and privacy challenges. For example, machine learning techniques are being applied to solve security problems such as malware analysis and insider threat detection. However, there is also a major concern that the machine learning techniques themselves could be attacked. Therefore, the machine learning techniques are being adapted to handle adversarial attacks. This area is known as adversarial machine learning. In addition, privacy of the individuals is also being violated through these machine learning techniques as it is now possible to gather and analyze vast amounts of data and therefore privacy enhanced data science techniques are being developed. Finally, Machine Learning techniques have to be fair and not discriminate. They also have to produce accurate results. Integrating Machine Learning with features like Security, Privacy, Integrity and Fairness have come to be known as Trustworthy Machine Learning,
With the advent of the web, computing systems are now being used in every aspect of our lives from mobile phones to smart homes to autonomous vehicles. It is now possible to collect, store, manage, and analyze vast amounts of sensor data emanating from numerous devices and sensors including from various transportation systems. Such systems collectively are known as the Internet of Transportation, which is essentially the Internet of Things for Transportation, where multiple autonomous transportation systems are connected through the web and coordinate their activities. However, security and privacy for the Internet of Transportation and the infrastructures that support it is a challenge. Due to the large volumes of heterogenous data being collected from numerous devices, the traditional cyber security techniques such as encryption are not efficient to secure the Internet of Transportation. Some Physics-based solutions being developed are showing promise. More recently, the developments in Data Science are also being examined for securing the Internet of Transportation and its supporting infrastructures. Our goal is to develop smart technologies for a Smart World.
To assess the developments on the integration of Machine Learning and Security over the past decade and apply them to the Internet of Transportation, the presentation will focus on three aspects. First it will examine the developments on Trustworthy Machine Learning including aspects of insider threat detection as well as the advances in adversarial machine learning. Some developments on privacy aware and policy-based data management frameworks will also be discussed. Second it will discuss the developments on securing the Internet of Transportation and its supporting infrastructures and examine the privacy implications. Finally, it will describe ways in which Trustworthy Machine Learning could be incorporated into the Internet of Transportation and Infrastructures.
Biography of Dr. Bhavani Thuraisingham
Dr. Bhavani Thuraisingham (aka Dr. Bhavani) is the Founders Chair Professor of Computer Science, the Founding Executive Director of the Cyber Security Research and Education Institute, and the Co-Director of the Women in Cyber Security and Women in Data Science Centers at the University of Texas at Dallas. She is also a visiting senior research fellow at Kings College, the University of London since 2015 conducting research on the foundations of IoT and was a Cyber Security Policy Fellow at the New America Foundation focusing on workforce development 2017-8. She is also a Member of the Faculty of Computer Science at the University of Dschang Cameroon, Africa since 2021 giving lectures (pro-bono) on Trustworthy Machine Learning, She is an elected Fellow of several prestigious organizations including the ACM, the IEEE, the AAAS and the NAI (National Academy of Inventors). Her research, development and education efforts have been on integrating cyber security and data science/machine learning for the past 37 years including at Honeywell Inc., The MITRE Corporation, the National Science Foundation, and Academia. Dr. Bhavani has received several awards including the IEEE Computer Society’s 1997 Technical Achievement Award, ACM SIGSAC 2010 Outstanding Contributions Award, 2011 AFCEA Medal of Merit, 2013 IBM Faculty Award, 2017 ACM CODASPY (Data and Applications Security and Privacy) Lasting Research Award, the 2017 Dallas Business journal Women in Technology Award, and the 2019 IEEE ComSoc Technical Recognition Award for Communications and Information Security. She has delivered around 200 keynote and featured addresses, and over 100 panel presentations, authored 16 books, published over 130 journal articles and over 300 conference papers. Dr. Bhavani received her PhD in Computability Theory from the University Wales, UK and the prestigious earned higher doctorate (D.Eng) form the University of Bristol, England for her published work in Secure Data Management.
Dr. X. Sean Wang
Fellow of CAAI and CCF, ACM Member, IEEE Senior Member
School of Computer Science, Fudan University, China
Title: Cloud Computing from a Task Centric Perspective
Cloud computing has become basic infrastructure that provides the computing needs for all sorts of applications. However, the model of cloud computing services seems still based on securing a single cloud service provider before launching tasks. This model requires a deep understanding of what services to acquire. In this talk, we try to argue for a task centric view, namely to envision a system that allows an understanding of the computing needs of a task (e.g., via automated or artificial annotation) and provides an automated process of acquiring or accepting suitable synchronous or asynchronous services from perhaps heterogeneous computing providers.
Biography of Dr. X. Sean Wang
X. Sean Wang is Professor at the School of Compute Science, Fudan University, a CAAI and CCF Fellow, ACM Member, and IEEE Senior Member. His research interests include data analytics and data security. He received his PhD degree in Computer Science from the University of Southern California, USA. Before joining Fudan University in 2011 to be the dean of its School of Computer Science and the Software School, he served as the Dorothean Chair Professor in Computer Science at the University of Vermont, USA, and as a Program Director at the National Science Foundation, USA. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation CAREER award. He's a former chief editor of the Springer Journal of Data Science and Engineering. He's currently on the steering committees of the IEEE ICDE and IEEE BigComp conference series, and past Chair of WAIM Steering Committee.