I am interested in the intersection of big data, data science, privacy, security, public policy, and law. At U-M, this includes co-convening the Dissonance Event Series, a multi-disciplinary collaboration of faculty and graduate students that explore the confluence of technology, policy, privacy, security, and law. I frequently guest lecture on these subject across campus, including at the School of Information, Ford School of Public Policy, and the Law School.
Accomplishments and Awards
My research spans security, privacy, and optimization of data collection particularly as applied to the Smart Grid, an augmented and enhanced paradigm for the conventional power grid. I am particularly interested in optimization approaches that take a notion of security and/or privacy into the modeling explicitly. At the intersection of the Intelligent Transportation Systems, Smart Grid, and Smart Cities, I am interested in data privacy and energy usage in smart parking lots. Protection of data and availability, especially under assault through a Denial-of-Service attacks, represents another dimension of my area of research interests. I am working on developing data privacy-aware bidding applications for the Smart Grid Demand Response systems without relying on trusted third parties. Finally, I am interested in educational and pedagogical research about teaching computer science, Smart Grid, cyber security, and data privacy.
This figure shows the data collection model I used in developing a practical and secure
Machine-to-Machine data collection protocol for the Smart Grid.
Di Ma is currently an Associate Professor in the Computer and Information Science (CIS) Department, College of Engineering and Computer Science (CECS), at the University of Michigan-Dearborn. She is also serving as the Interim Associate Dean for Graduate Education and Research and the director of the Cybersecurity Center for Education, Research, and Outreach, CECS. Dr. Ma received her PhD degree from the University of California, Irvine, in 2009. She is the recipient of the Trevor O. Jones Outstanding Paper Award from the Society of Automobile Engineers (SAE) in 2019, the Distinguished Research Award from CECS in 2017, and the Tan Kah Kee Young Inventor Award in 2004. She is broadly interested in the general area of security, privacy, and applied cryptography. Her research spans a wide range of topics, including connected and autonomous vehicle security, smartphone and mobile device security, RFID and sensor security, data privacy, and so on. Her research is supported by NSF, NHTSA, AFOSR, Intel, Ford, and Research in Motion. She was with IBM Almaden Research Center in 2008 and the Institute for Infocomm Research, Singapore in 2000-2005
My research interests include security, privacy, and adversarial machine learning.
More information about some of the current projects can be found at https://iotsecurity.engin.umich.edu.
9.9.2020 MIDAS Faculty Research Pitch Video.
An adversarial testing pipeline to fool machine learning classifiers. A STOP sign is being modified using stickers so that a state-of-the-art classifier is fooled into thinking it is a 45 SPEED LIMIT sign. For more details, visit https://iotsecurity.engin.umich.edu (Robust Physical Perturbations tab)
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