Yu-Chi Chen
Yu-Chi Chen
Associate Professor, NTUT Dept. of Computer Science & Information Engineering
National Taipei University of Technology, Taipei Tech

Yu-Chi Chen received his Ph.D. in Computer Science and Engineering from National Chung Hsing University. Currently he serves as an Associate Professor in the Department of Information Engineering at National Taipei University of Technology. 

Experience:

  • Associate Professor and Assistant Professor at the Department of Computer Science and Engineering.
  • Yuan Ze University, Postdoctoral Researcher at Academia Sinica’s Institute of Information Science. 

He has focused on research in cryptography and information security and recently has expanded his research to the application of privacy-enhancing technologies in artificial intelligence. He continues to execute various National Science and Technology Council research projects and industry-academia collaboration projects. In recent years, he has been a co-PI of the Ministry of Education – Information Security Incubation Program, organizing annual events such as AIS3, AIS3 Junior, MyFirstCTF, AIS3 EOF, and is committed to promoting cybersecurity awareness and community/society, such as organizing AIS3 Club and 3SIA cybersecurity games.

SPEECH
5/16 (Thu.) 10:15 - 11:15 7F 702 Privacy Enhancing Technology Forum
Overview of Privacy-Enhancing Technologies

Privacy-enhancing technologies (PETs) are technologies that embody the fundamental principles of data protection by minimizing personal data use, maximizing data security, and enhancing individual agency. PETs protect the privacy of personal information of users authorized by services or applications. They employ techniques to minimize the possession of personal data by information systems without losing functionality. However, there is no unified definition of PETs to quantify privacy since the objectives and scenarios depend on practical applications. In this lecture, we start from the motivation for privacy, illustrate why PETs are necessary through real-life events, and then introduce an overview of existing privacy solution technologies, including federated learning, secure multi-party computation, homomorphic encryption, differential privacy, and zero-knowledge proofs, among others.