Research

Our Research

Our research aims at building data-driven experimental computer systems to improve Cybersecurity, User Privacy, and Sustainability of Cyber-Physical Systems (CPS) and the Internet of Things (IoT). Our work has been spanning multiple technical fields, including AI@Edge, Tiny Machine Learning, Federated/Split Learning, Faster CNN, and Multi-Agent Reinforcement Learning.

The Internet of Things (IoT) devices have been increasingly deployed in smart homes for automation. Unfortunately, extensive recent research shows that external on-path adversaries can infer and fingerprint user sensitive in-home activities by analyzing IoT network traffic rates alone. Most recent traffic padding-based defending approaches cannot sufficiently protect user privacy with reasonable traffic overhead. In addition, these approaches typically assume the installation of additional hub hardware in smart homes to host their traffic padding-based defending approaches. To address these issues, we design and implement multiple systems (e.g., PAROS in ICCCN’23, PrivacyGuard in IPSN’21, TrafficSpy in CNS’22) that can enable smart home users to significantly reduce private information leaked through IoT network traffic rates.

[ICCCN’23] PAROS: The Missing “Puzzle” in Smart Home Router Operating Systems.
Keyang Yu, Dong Chen.
In Proc. of the 32nd International Conference on Computer Communications and Networks (ICCCN 2023), July 24 – July 27, 2023, Waikiki Beach, Honolulu, HI, USA. Acceptance Rate = 30.38%.

[CNS’22] TrafficSpy: Disaggregating VPN-encrypted IoT Network Traffic for User Privacy Inference.
Qi Li, Keyang Yu, Dong Chen, Mo Sha and Long Cheng.
In Proc. of the 10th IEEE Conference on Communications and Network Security (CNS 2022), 3-5 October 2022, Austin, Texas, USA. Acceptance Rate = 35.25%.

[IPSN’21] PrivacyGuard: Enhancing Smart Home User Privacy.
Keyang Yu, Qi Li, Dong Chen, Mohammad Rahmann, and Shiqiang Wang.
In Proc. of the 20th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN’21, May 18–21, 2021, Nashville, TN, USA. Acceptance Rate = 24.76%. (Source Code and Data)

[ICDCS’18] Private Memoirs of IoT Devices: Safeguarding User Privacy in the IoT Era.
Dong Chen, Phuthipong Bovornkeeratiroj, David Irwin and Prashant Shenoy.
In Proc. of the 38th IEEE International Conference on Distributed Computing Systems (ICDCS’18), July 2 – 5, 2018, Vienna, Austria.

Homeowners are increasingly deploying grid-tied solar systems due to the rapid decline in solar module prices. The energy produced by these solar-powered homes is monitored by utilities and third parties using networked energy meters, which record and transmit energy data at fine-grained intervals. Such energy data is considered anonymous if it is not associated with identifying account information, e.g., a name and address. Thus, energy data from these “anonymous” homes is often not handled securely: it is routinely transmitted over the Internet in plaintext, stored unencrypted in the cloud, shared with third-party energy analytics companies, and even made publicly available over the Internet. Extensive prior work has shown that energy consumption data is vulnerable to multiple attacks, which analyze it to reveal a range of sensitive private information about occupant activities.Our research aims at prevent user private information leakage from net meter data.

 

 

Smart cities, utilities, third-parties, and government agencies are having pressure on managing stochastic power generation from distributed rooftop solar photovoltaic (PV) arrays, such as predicting and reacting to the variations in electric grid. Recently, there is a rising interest to identify solar PV arrays automatically and passively. Traditional approaches such as online assessment and utilities interconnection filings are time consuming and costly, and limited in geospatial resolution, and thus do not scale up to every location. Significant recent work focuses on using aerial imagery to train machine learning or deep learning models to automatically detect solar PV arrays. Unfortunately, these approaches typically require Very High Resolution (VHR) images and human handcrafted solar PV array templates for training, which have a minimum cost of $15 per km 2 and are not always available at every location.To address the problem, we design muiltiple solar PV detection systems and solar PV generated energy trading systems.

[IoTDI’23] SolarDetector: Automatic Solar PV Array Identification using Big Satellite Imagery Data.
Qi Li, Sander Schott, and Dong Chen.
In Proc. of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation, May 9-12, 2023, San Antonio, Texas, part of CPS-IoT Week’23, Acceptance Rate = 30.27%.

[BuildSys’20] SolarTrader: Enabling Distributed Solar Energy Trading in Residential Virtual Power Plants.
Yuzhou Feng, Qi Li, Dong Chen, and Raju Rangaswami.
In Proc. of the 7th ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys 2020), Acceptance Rate = 24.3%, November 18–20, 2020, Virtual Event, Japan. (Source Code and Data). The Best Paper Award at ACM BuildSys’20.

[IGSC’20] SolarDiagnostics: Automatic Rooftop Solar Photovoltaic Array Damage Detection.
Qi Li, Keyang Yu and Dong Chen.
In Proc. of the Eleventh IEEE International Green and Sustainable Computing Conference, IGSC’20, Oct 19-22, Acceptance Rate = 23%. (Source Code and Data)

[IPSN’20] SolarFinder: Automatic Detection of Solar Photovoltaic Arrays.
Qi Li, Yuzhou Feng, Yuyang Leng, and Dong Chen.
In Proc. of the 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN’20, April 21-24, 2020, Sydney, Australia, Acceptance Rate = 21.33%. (Source Code and Data)