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!
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.