Privacy Preserving Protest Dynamics

Limited data-availability and ethical concerns regarding individual privacy hinder large-scale understanding of protests using social media data. Current visual methods for human image understanding are capable of re-identifying faces, understanding complex scenes and objects, and associating them together. This presents an ethical dilemma, as large vision models can memorize and leak sensitive information from training data. To our knowledge, unfortunately, no existing protest analysis method takes privacy into consideration. To close the gap in the literature, we propose a simple and robust framework for understanding protest dynamics with privacy preservation, namely privacy preserving protest dynamics (P3D). Our P3D framework aims to replace private data with well labeled synthetic reproductions using conditional image synthesis. Extensive evaluation of the proposed P3D method demonstrates its ability to generate realistic and diverse imagery, and its efficacy in utility and privacy for downstream protest analysis. Moreover, unlike other private models, P3D can provide a moderate level of privacy by modeling complex visual features in protest images while maintaining downstream utility. Code is available at https://github.com/cgarchbold/P3D.