Privacy Risks in Event-Based Cameras: The Role of Sensor Configuration

Abstract

Event-based cameras—sensors that asynchronously record pixel-level brightness changes rather than full image frames—are often assumed to be privacy-preserving due to their sparse visual output. This thesis investigates how physical sensor configurations, including temporal bandwidth, contrast thresholds, leak noise, and spatial resolution, affect the trade-off between data utility and biometric privacy risk. We introduce a cross-domain evaluation framework that measures identity leakage using reconstructed event streams and a frozen pre-trained face recognition model. Our results show that event streams retain sufficient facial structure for accurate identification. We further identify a privacy paradox in which reducing temporal bandwidth increases attacker performance by denoising the signal, while background leak noise effectively disrupts reconstruction and lowers identification accuracy. Privacy effectiveness also varies substantially with subject motion, and the apparent benefits of resolution scaling are largely explained by domain mismatch. Overall, the findings suggest that static sensor configurations cannot guarantee anonymity, highlighting the need for threat-aware sensor design and adaptive privacy safeguards. The code used in this research is available at: [https://github.com/Stunner070/research-project-bsc-cse-tudelft](https://github.com/Stunner070/research-project-bsc-cse-tudelft).