Haojian Jin

Bio: Haojian Jin is a final year Ph.D. student in the Human-Computer Interaction Institute at Carnegie Mellon University, advised by Jason Hong and Swarun Kumar. Haojian’s research explores new software architecture and toolkits that make it easier for users, developers, and auditors to protect users’ privacy. His work has been recognized with a UbiComp Gaetano Borriello Outstanding Student Award, Research Highlights at Communication of ACM and GetMobile, and best paper awards at Ubicomp and ACM Computing Reviews.

Talk Title: My Data is None of Your Business: Separation of Concerns for Privacy through Modular Privacy Flows.

Talk Abstract: This wide-scale deployment of tiny sensors, coupled with improvements in recognition and data mining algorithms, will enable numerous new applications for personal and societal benefits. But, we have also seen many undesired data-driven applications deployed, such as price discrimination, shopping behavior persuasion. Once one’s data is out of users’ direct control, it may potentially be used at places and times far removed from its original context. How can we computer scientists assure users that a data-driven world is the one everyone wants to live in?

In this talk, I will introduce my thesis work on separating concerns for privacy through a new software design pattern, named Modular Privacy Flows. Rather than continuing to build privacy support in an ad-hoc manner, my research demonstrates how we can separate the privacy logic from the application logic. This separation can help users gain independent and unified control of their data while reducing the burdens of developers and auditors on ensuring privacy.