Abstract

Privacy-enhancing technologies (PETs) are technologies that embody fundamental data protection principles by minimizing personal data use, maximizing data security, and empowering individuals. PETs keep the privacy of users' personal information handled by outsourced services or applications. They use techniques to minimize an information system's possession of personal data without losing functionality. However, PETs do not have a unified definition to quantify privacy, since the purposes and scenarios depends on the applications. In this talk, we start from the motivations of privacy, and then show real-life events to claim PETs should be necessary. We will cover the technical glance at a few existing privacy solutions, including Federated Learning, Secure Multiparty Computation, Homomorphic Encryption, Differential Privacy, and Zero Knowledge Proof. 

Bio.

Yu-Chi Chen received B.S., M.S. and Ph.D. degrees from Department of Computer Science and Engineering, National Chung-Hsing University, Taiwan, in 2008, 2009 and 2014, respectively. In 2013, he was a visiting scholar at Department of Electrical Engineering, University of Washington. He was a postdoctoral fellow at the Institute of Information Science, Academia Sinica, Taiwan, from 2014 to 2017. He was on the faculty of the Department of Computer Science and Engineering, Yuan Ze University, Taiwan, from 2017 to 2022. He is currently an associate professor at Department of Computer Science and Information Engineering, National Taipei University of Technology, Taiwan. His research interests include cryptography and information security.

All faculty and students are welcome to join.