Liangqin Ren

Liangqin Ren

University of Kansas

I am a final-year Ph.D. candidate in Electrical Engineering and Computer Science (EECS) at the University of Kansas, advised by Prof. Fengjun Li and Prof. Bo Luo. My research focuses on the security and privacy of machine learning systems, including model protection, Deepfake defenses, and copyright protection. I am also interested in recommender systems, large language models, and large-scale machine learning applications.

Education

  • Ph.D. in Computer Science, University of Kansas (Aug 2021 – Dec 2026 (Expected))
  • M.Eng. in Computer Technology, University of Chinese Academy of Sciences (Aug 2017 – Jun 2020)
  • B.Eng. in Network Engineering, Shandong University of Science and Technology (Aug 2013 – Jun 2017)

Publications

  • Liangqin Ren, Zeyan Liu, Ye Wang, Yuxin Chen, Fengjun Li, and Bo Luo. PhantomSeal: Proactive Deepfakes Defense with Identity/Context Protection and Forensic Tracing. In Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS), The Hague, Netherlands, 2026. [To Appear]
  • Xu, Xin, Zhen Yang, Quanwei Cai, Jingqiang Lin, Liangqin Ren, Bo Chen, and Yongfeng Huang. Enforcing cryptographic distributed-VCS access control with no trust on servers. Journal of Information Security and Applications (JISA), 93 (2025): 104103. [Link]
  • Yuying Li, Zeyan Liu, Junyi Zhao, Liangqin Ren, Fengjun Li, Jiebo Luo, and Bo Luo. The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking. In Proceedings of the European Symposium on Research in Computer Security (ESORICS), Bydgoszcz, Poland, 2024. [Link]
  • Liangqin Ren, Zeyan Liu, Fengjun Li, Kaitai Liang, Zhu Li, and Bo Luo. PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption. In Proceedings of Privacy Enhancing Technologies Symposium (PETS), Bristol, UK, 2024. [Link, PDF, Codes]

Teaching

  • Teaching Assistant, EECS 348/448 Software Engineering, University of Kansas (Fall 2022 – Spring 2026)

Services

  • Reviewer for TDSC (2025-2026), ISCAS (2024–2025), and KSEM (2024).
  • Session Moderator for SecureComm 2022.

Internships

  • Applied Scientist Intern, Amazon, Seattle, WA (May 2025 – Aug 2025)
    • Leveraged LLMs to infer user context patterns and enhanced retrieval candidate ranking by incorporating contextual information.
  • Applied Scientist Intern, Amazon, Seattle, WA (Jun 2024 – Sep 2024)
    • Extracted user emotions from video reviews using NLP and LLM techniques and applied them to enhance long-tail recommendation performance.
  • Software Development Engineer Intern, Baidu, Beijing (Jan 2021 – Jun 2021)
    • Developed Baidu translation SDKs for mobile devices, enabling integration of machine translation into Android and iOS applications.