标题：An End-User and Query Generalization Tool for Privacy Enhancement in Web Search
Web search engines often collect personalized search results to construct a user’s profile. The lack of transparency about what information is stored, how it is used and with whom it is shared, limits the perception of privacy. There is a need to protect users’ privacy during web searching, especially after the release of GDPR. In this talk, I introduce a tool of PrivacySearch for such purpose, which is implemented as a browser plugin for Google Chrome, enables users to generalize the queries sent to a search engine in an automatic fashion and real time, without the need for any kind of infrastructure or external databases, according to simple and intuitive privacy criteria. Experimental results demonstrate the technical feasibility and suitability of our solution. In the end, we discuss some challenges in this direction, especially how to make a balance between search precise and user privacy.
报告人简介：Dr. Weizhi Meng is currently an assistant professor in the Cyber Security Section, Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU),Denmark. He obtained his Ph.D. degree in Computer Science from the City University of Hong Kong (CityU), Hong Kong. Prior to joining DTU, he worked as a research scientist in Infocomm Security (ICS) Department, Institute for Infocomm Research, A*STAR, Singapore for over two years, and as a senior research associate in CityU.
His primary research interests are cyber security and intelligent technology in security including intrusion detection, mobile security, biometric authentication, HCI security, cloud security, trust computation, web security, malware analysis, and privacy enhancement. He also shows a strong interest in applied cryptography. He won the Outstanding Academic Performance Award during his doctoral study, and is a recipient of the Hong Kong Institution of Engineers (HKIE) Outstanding Paper Award for Young Engineers/Researchers in 2014 and 2017.