AnonyNoise: Anonymizing Event Data with Smart Noise to Outsmart Re-Identification and Preserve Privacy

Katharina Bendig, René Schuster, Nicole Thiemer, Karen Joisten, Didier Stricker; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 3159-3161

The increasing capabilities of deep neural networks for re-identification combined with the rise in public surveillance in recent years pose a substantial threat to individual privacy. Event cameras were initially considered as a promising solution since their output is sparse and therefore difficult for humans to interpret. However recent advances in deep learning proof that neural networks are able to reconstruct high-quality grayscale images and re-identify individuals using data from event cameras. In our paper we contribute a crucial ethical discussion on data privacy and present the first event anonymization pipeline to prevent re-identification not only by humans but also by neural networks. Our method effectively introduces learnable data-dependent noise to cover personally identifiable information in raw event data reducing attackers' re-identification capabilities by up to 60% while maintaining substantial information for the performing of downstream tasks. Moreover our anonymization generalizes well on unseen data and is robust against image reconstruction and inversion attacks. Code: https://github.com/dfki-av/AnonyNoise