The I-Privacy lab works to protect privacy for everyone who is surfing the Internet and entering the era of Internet of Things. We work on a variety of cutting-edge research topics such as mobile app security, location privacy, image privacy protection on social network, ransomware detection and defense, attacking and defending artificial intelligence, security and privacy issues in autonomous cars, security and privacy issues in big data processing. We have a group of talented graduate students and we offer different types of scholarship such as the following NSF SFS program.
Ongoing Research Projects
The goal of this project is to overcome the following challenges which have not been well studied: (i) Privacy protection for human subjects and sensitive objects in the background of the images which are usually ignored by the existing works; (ii) Consideration of location-dependent image sensitivities whereby images taken at certain places (e.g., pubs, hospitals) may impact privacy of some people in the images who do not want their occurrences or co-occurrences at those locations to be known; (iii) Need for a comprehensive privacy model that can determine the extent to which the privacy protection mechanism reduces practical privacy risks; (iv) Enforcing the privacy protection that conforms with different privacy needs of multiple people in the same image.
The objective of this project is to design an intelligent deepfake detector, namely DeepDetect, which will be capable of assessing the integrity of digital visual content and automatically detect fake images or videos in the real time to prevent them from spreading. The uniqueness of the proposed DeepDetect is its ability of self-learning and self-evolving to capture fake visual content generated by new (currently unknown) deepfake algorithms over time.
In this project, we aim to develop comprehensive message routing solution to provide the fundamental support of information management for the Internet of vehicles. We have designed approaches that deliver messages via a self-organized moving-zone-based architecture formed using pure vehicle-to-vehicle communication and integrates moving object modeling and indexing techniques to vehicle management. It can significantly reduce the communication overhead while providing higher delivery rates. For the successful roll out of IoV applications, we also need to ensure the identity and location privacy of the participating vehicles, for which we are working on a suite of security and privacy protection mechanisms.
Jian K. (PhD) , work on "Internet of vehicles", expected graduation date May 2022
Sara N. (PhD), work on "machine learning attacks", expected graduation date Dec 2021
Maya C. (PhD), work on "machine learning attacks", expected graduation date Dec 2022
Chaoquan C. (PhD), work on "large-scale trajectory analysis", expected graduation date Dec 2022
Ke L. (PhD) , work on "machine learning attacks", expected graduation date May 2023
Tyler N. (PhD), work on "privacy-preserving contact tracing", expected graduation date May 2023
Ethan O. (PhD), work on "privacy protection for social media users", expected graduation date May 2023
Stephen O. (PhD), work on "privacy protection for social media users", expected graduation date May 2023
Eric W. (PhD, co-advisor), work on "applied cryptography", expected graduation date May 2023
Alian Y. (PhD), work on "Internet of vehicles", expected graduation date Dec 2024
Cameron B. (Dual-enrolled in PhD), work on "machine learning attacks", expected graduation date May 2025
Chloe J. (Dual-enrolled in PhD), work on "deepfake generation and detection", expected graduation date May 2025
NSF Scholarship for Service (SFS) Program
Students who are interested in applying for the Scholarship for Service Program must complete the following documents.
SFS application is always open until the positions are filled. To allow sufficient time for evaluation and paper work, please submit your application at least a couple of months earlier before the semester that you wish your scholarship to begin.
Please email your application package to Dr. Dan Lin (lindan @ missouri.edu)