Yidan Hu Headshot

Yidan Hu

Assistant Professor, Cybersecurity

Department of Cybersecurity
Golisano College of Computing and Information Sciences

Office Location

Yidan Hu

Assistant Professor, Cybersecurity

Department of Cybersecurity
Golisano College of Computing and Information Sciences

Education

B.E., M.E, Hangzhou Dianzi University; Ph.D., University of Delaware

Bio

Yidan Hu is an Assistant Professor in the Department of Cybersecurity at Rochester Institute of Technology. She received her Ph.D. degree in Computer Science from the University of Delaware in 2021, supervised by Dr. Rui Zhang. She received the B.E. degree and M.E. degree both in Computer Science from Hangzhou Dianzi University in 2013 and 2016, respectively.

Her primary research interests are data privacy and cybersecurity, with the current focus on security and privacy in network and distributed systems, including wireless networks, cognitive radio networks, mobile crowdsourcing, mobile edge computing, and cloud computing systems.


Areas of Expertise

Currently Teaching

CSEC-472
3 Credits
This course covers the theory, design, and implementation of authentication and access control systems with an emphasis on trust and secure protocol design. Students will examine authentication protocols, password systems, multi-factor and biometric approaches, single sign-on, post-quantum methods, and formal models of access control. The course emphasizes applied analysis and implementation, preparing students to evaluate and communicate about authentication and access control in evolving security environments.
CSEC-721
3 Credits
In today’s data-driven world, protecting sensitive information is more critical than ever. This research seminar explores the latest developments and challenges in privacy-enhancing technologies, emphasizing both theoretical foundations and practical methods. Students will examine how privacy risks arise in networked and distributed systems and investigate techniques such as anonymization, perturbation, cryptographic frameworks, and distributed privacy-preserving computation. Through critical analysis of current literature and research-driven projects, participants will learn to evaluate trade-offs, compare approaches, and propose strategies that advance the state of the art in privacy-preserving systems.

In the News