Privacy-preserving emotion detection for crowd management

Abstract

Emotion detection plays a vital role in crowd management as it enables social event organizers to detect the actions of masses and react accordingly. There are several approaches to detect emotions in a crowd, including surveillance cameras, human observers and sensors. One other approach to gather emotion data is self-reporting. A recent study showed that self-reporting is feasible, reliable and efficient. However, there is a strong privacy concern among people that risks the use of such self-reporting mechanisms in wide use. In this work, we address the privacy aspect of self-reporting mechanism and propose a cryptographic approach that hides the sensitive data from the organizers but permits to compute statistical data for crowd management. The feasibility of using cryptography in real life for privacy protection is also investigated in terms of complexity. © 2014 Springer International Publishing.