SensorSift: Balancing Privacy and Utility in Sensor Data

The rapid growth of sensors and algorithmic reasoning are creating an important challenge to find balance between user privacy and functionality in smart applications. To address this problem Miro Enev and collaborators have developed a quantitative framework called SensorSift which we recently published and have now made available as open source!

http://homes.cs.washington.edu/~miro/sensorsift/.

At the heart of our contribution is an algorithm which transforms raw sensor data into a ‘sifted’ representation which minimizes exposure of user defined private attributes while maximally exposing application-requested public attributes. We envision multiple applications using the same platform, and requesting access to public attributes explicitly not known at the time of the platform creation. Support for future-defined public attributes, while still preserving the defined privacy of the private attributes, is a central challenge that we tackle.

UW CSE’s security lab featured on David Pogue’s PBS NOVA Science NOW

David Pogue’s PBS NOVA Science NOW featured the work of UW CSE Security and Privacy Research Lab as the final segment of the episode “Can Science Stop Crime?”

Those featured include UW CSE faculty member Yoshi Kohno, UW CSE Ph.D. alum Dan Halperin, and UW CSE Ph.D. students Karl Koscher, Franzi Roesner, Alexei “Crash” Czeskis – and the work of these and others.

Watch this 12-minute PBS NOVA Science NOW segment!

Watch Can Science Stop Crime? on PBS. See more from NOVA scienceNOW.

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