name2---->(字串長度:0)---->Chen-Mou Cheng(字串長度:14)```anchor---->speaker-102(字串長度:11)---->chen-mou-cheng(字串長度:14)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->長庚大學人工智慧學系(字串長度:30)---->Chang Gung University(字串長度:21)```position---->教授(字串長度:6)---->Professor, Department of Artificial Intelligence(字串長度:48)```desc---->(字串長度:0)---->The intersection of LLM and privacy-preserving techniques is an emerging research field, particularly relevant for domain-specific applications.  There is increasing interest in methodologies that enable collaborative or multi-tenant training and inference while addressing both trust and efficiency concerns.  In this talk, I will give a high-level tutorial on several cryptographic techniques that may help the audience better understand the state-of-the-art research in the area of secure AI inference.(字串長度:505)```me_desc---->(字串長度:0)---->Chen-Mou Cheng received his B.S. and M.S. degrees in electrical engineering from National Taiwan University in 1996 and 1998, respectively, as well as his PhD in computer science from Harvard University in 2007.  He is currently a Professor at the Department of Artificial Intelligence, Chang Gung University.  Between 2022 and 2024, he was the Chief Cryptographer at BTQ Technologies, Inc., a publicly-listed Canadian startup focusing on building post-quantum cryptographic technologies and solutions for blockchain applications.  Between 2007 and 2022, he taught cryptography at National Taiwan University, Osaka University, and Kanazawa University.(字串長度:651)```talkfile_link---->(字串長度:0)---->V(字串長度:1)