Advanced Distributed Learning Initiative Technical Webinar
User-Tailored Privacy for TLA (Parts I & II)
Part I – Wednesday, 14 NOV Part II – Wednesday, 21 NOV
Parts I & II – 1100-1200 EST
If you are developing a user-facing information system, chances are that you have to deal with users’ privacy concerns. You can offer users a privacy-setting interface, but studies show that users find privacy settings notoriously difficult to control. How can you actively help users find their way among the myriad of privacy controls your system provides?
This two-part webinar series discusses one particular human-centric solution to reduce users’ privacy concerns: 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. This approach strikes a balance between the user autonomy afforded by the notion of “transparency and control” and the effortless privacy protection provided by the “privacy nudging” paradigm.
Subject: The first part of this series covers the general topic of User-Tailored Privacy, covering the motivation for implementing User-Tailored Privacy, its definition, theoretical background, and basic components of “measure”, “model”, and “adapt”.
The second part focuses on the potential implementation of User-Tailored Privacy in TLA (and other advanced distributed learning systems).
Audience: The first part is open to any developer, researcher, or manager who is interested in learning about a novel way to deal with end-user privacy.
The second part is particularly interesting for anyone associated with the Total Learning Architecture or a similar advanced distributed learning system. Attending part 1 is advised (but not required) for those interested in attending part 2.
Speaker Bio: Bart Knijnenburg is an assistant professor in Human-Centered Computing at Clemson University. Bart received his PhD from the University of California, Irvine, where his research focused on personalized privacy decision support systems and the user-centric evaluation of recommender systems. He is the co-director of the Humans-And-Technology Lab, where he works on user-centric privacy and recommender systems research in close collaboration with several companies and institutes. Bart is the author of over 50 papers published in journals, books, and conference proceedings. He is an expert reviewer on the topic of user research statistics for several conferences and journals, and a consultant on several academic and industry projects.