AnonyNoise: Anonymizing Event Data with Smart Noise to Outsmart Re-Identification and Preserve Privacy
This addresses privacy threats from deep learning in surveillance for individuals, offering a novel solution to a known bottleneck.
The paper tackles the problem of re-identification from event camera data by presenting an anonymization pipeline that reduces attackers' re-identification capabilities by up to 60% while preserving information for downstream tasks.
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