Florida International University
Dong Chen is currently an Assistant Professor in the School of Computing and Information Sciences at Florida International University. His research focuses on data analytics, cybersecurity, and privacy implications of Cyber-Physical Systems. He received his Ph.D. in Electrical and Computer Engineering in 2018 from University of Massachusetts Amherst. Before that, he completed his Ph.D. in Computer Science in 2014 from Northeastern University, China. He has published 26 research papers in these and related areas over the past decade. In addition, his work on energy data analytics has been applied to real-world energy data, which he has made publicly available.
The rise of the Cyber-Physical Systems (CPS) holds great promise to transform people’s lives by making society more efficient in many areas, including energy, transportation, healthcare, manufacturing, etc. At their core, CPS devices use sensors to collect data on real-world physical processes and then transmit it over the Internet to cloud servers, which store, process, and learn from the data to better optimize these processes. Unfortunately, CPS devices also expose users to multiple new types of privacy attacks. In particular, the sensor data collected from CPS devices can indirectly reveal a variety of sensitive private information. In this talk, I will discuss my recent work on sensor data privacy in the context of Cyber-Physical Energy Systems (e.g., smart grid) to provide examples of the surprising types of private information (e.g., location, occupancy, behavior, appliance energy signatures etc.) that I can glean from seemingly innocuous CPS data. For example, my recent work – SunSpot has demonstrated that solar generation data at a particular site embeds the location of that site, and this location information can be extracted from the solar generation trace using advanced data analytics and machine learning techniques. Then, I will discuss a low-cost obfuscation defense-Combine Heat and Privacy (CHPr) that I designed to preserve energy data privacy. CHPr can schedule large elastic heating loads already present in many homes (e.g., water heater) to reshape demand to protect occupancy effectively. Finally, I will discuss my future directions about cybersecurity and privacy research in energy systems and other intersect areas of CPS.