haojian@cs.cmu.edu
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In the next few decades, there will be orders of magnitude more sensors deployed worldwide. 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.
However, a major question here is, how we can address people's legitimate privacy concerns? In such a ubiquitously connected world, the cost of collecting, storing, inferring, searching, and sharing data are dramatically lowered, and we have already seen undesired data-driven applications deployed using just web-based tracking (e.g., 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.
My research moves the needle for privacy protection from three perspectives,
- How can we make it easier for developers/companies to protect users' privacy?
- How can we further motivate people to protect their own privacy?
- How can we create a new architecture that protects users' privacy natively?
I am a Ph.D. student in the Human-Computer Interaction Institute at Carnegie Mellon University, advised by Jason Hong and Swarun Kumar. I also collaborate closely with Yuvraj Agarwal over the years. My research is about building systems at the intersection of Human-Computer Interaction, Mobile Computing and Privacy & Security. Before coming to CMU, I had been a full-time researcher at Yahoo Research for 3 years. At Yahoo, I developed key algorithms in visual search, cryptography, speech recognition and large-scale data mining. I also have run a startup and interned at Microsoft Research Asia, Bosch Research and Chinese Academy of Science before I started my Ph.D..