Privacy Preservation in Large Language Models (LLMs)
Speaker: 
My T. Thai
Professor
Department of Computer &
Information Science & Engineering
University of Florida
| Date: Time: Venue: | | 3 August 2026, Monday 10:00am – 11:00am School of Computing & Information Systems 1 (SCIS 1)
Level 4, Meeting Room 4-4
Singapore Management University
Singapore 178902
Please register by 31 July 2026 
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About the Talk
Large Language Models (LLMs) have revolutionized artificial intelligence but introduced profound privacy vulnerabilities, including data leakage, model inversion, and the inadvertent exposure of sensitive information. As LLMs integrate into high-stakes applications, mitigating these risks is paramount. This talk explores the fundamental privacy challenges inherent in LLM operations and introduces NOIR, the first privacy-preserving LLM model. To prevent prompting contents exposure to honest-but-curious cloud service providers, NOIR utilizes a secure, distributed architecture that transmits only encoded embeddings. By employing local differential privacy at the token embedding level alongside a data-independent, randomized tokenizer, NOIR effectively shields both proprietary prompts and resultant answers. The talk will demonstrate how NOIR achieves an optimal balance, providing rigorous privacy guarantees without compromising computational efficiency or downstream model performance.
About the Speaker
My T. Thai is a Research Foundation Professor, Associate Director of UF Nelms Institute for the Connected World, and a Fellow of IEEE and AAIA. Dr. Thai is a leading authority who has done transformative research in Trustworthy AI and Optimization, especially for complex systems with applications to healthcare, social media, critical networking infrastructure, and cybersecurity. The results of her work have led to 9 books and 350+ publications in highly ranked international journals and conferences, including several best paper awards from the IEEE, ACM, and AAAI.
In responding to a world-wide call of responsible and safety AI, Dr. Thai is a pioneer in designing deep explanations for black-box ML models, while defending against explanation-guided attacks, evident by her Distinguished Papers Award at the Association for the Advancement of Artificial Intelligence (AAAI) conference on AI, 2023. At the same year, she was also awarded an ACM Web Science Trust Test-of-Time award, for her landmark work on combating misinformation in social media. In 2022, she received an IEEE Big Data Security Women of Achievement Award. In 2009, she was awarded the Young Investigator (YIP) from the Defense Threat Reduction Agency (DTRA) and in 2010, she won the NSF CAREER Award.
She is presently the Editor-in-Chief of ACM Computing Surveys, Springer Journal of Combinatorial Optimization, IET Blockchain Journal, and book series editor of Springer Optimization and Its Applications.
