Tailoring Privacy to User Preferences and Privacy Regulations in Web Personalization

Students: Yang Wang, UC Irvine/ISR

Advisor: Alfred Kobsa, UC Irvine/ISR

Abstract:

Web personalization has demonstrated to be advantageous for both online customers and vendors. However, its benefits may be severely counteracted by privacy constraints. Personalized systems need to take users' privacy concerns into account, as well as privacy laws and industry self-regulation that may be in effect. In this paper, we first discuss how these constraints may affect web-based personalized systems. We then explain in what way current approaches to this problem fall short of their aims, specifically regarding the need to tailor privacy to the constraints of each individual user. We present a dynamic privacy-enhancing user modeling framework as a superior alternative, which is based on a software product line architecture. Our system dynamically selects personalization methods during runtime that respect users' current privacy concerns as well as the privacy laws and regulations that apply to them.

Bio:

Yang Wang is a PhD candidate in the Donald Bren School of Information and Computer Sciences of the University of California, Irvine. His broad research interests span across the fields of Human-Computer Interaction (HCI), Software Engineering (SE), E-Commerce and Applied Statistics. His PhD research focuses on mechanisms of reconciling web personalization with privacy constraints imposed by legal restrictions and by users' privacy preferences. He has also worked on online communities (such as blogsphere and online games) and digital money. He was a visiting researcher at Institute of Information Systems at Humboldt University in Berlin. He has performed research with several organizations, including CommerceNet, Fuji Xerox Palo Alto Lab (FXPAL), and Intel Research.