Privacy issues are an undying obstacle to the real-world implementation of information systems, from online retailers, to social networks, to smart home technology. Existing solutions to these privacy issues involve giving users more control over, and more information about, the privacy settings provided by these systems. In this talk, I will argue that these solutions fail when users with limited cognitive resources encounter systems with complex and far-reaching privacy implications. I will subsequently discuss a novel human-centric solution to improve users' privacy decisions: User-Tailored Privacy. User-Tailored Privacy is an approach to privacy that measures users’ privacy-related characteristics and behaviors, uses this as input to model their privacy preferences, and then provides them with adaptive privacy decision support. In effect, it applies data science as a means to support users’ privacy decisions.
Bart Knijnenburg is an Assistant Professor in Human-Centered Computing at the Clemson University School of Computing where he co-directs the Humans and Technology lab. He holds a B.S. in Innovation Sciences and an M.S. in Human-Technology Interaction from the Eindhoven University of Technology, The Netherlands, an M.A. in Human-Computer Interaction from Carnegie Mellon University, and a PhD in Information and Computer Sciences from UC Irvine. Bart works on privacy decision-making and user-centric evaluation of adaptive systems. His research has received funding from the National Science Foundation, the Department of Defense, and corporate sponsors.