Event Encryption: Rethinking Privacy Exposure for Neuromorphic Imaging

Abstract

Bio-inspired neuromorphic cameras sense illumination changes on a per-pixel basis and generate spatiotemporal streaming events within microseconds in response, offering visual information with high temporal resolution over a high dynamic range. Such devices often serve in surveillance systems due to their applicability and robustness in environments with high dynamics and strong or weak lighting, where they can still supply clearer recordings than traditional imaging. In other words, when it comes to privacy-relevant cases, neuromorphic cameras also expose more sensitive data and thus pose serious security threats. Therefore, asynchronous event streams also necessitate careful encryption before transmission and usage. This letter discusses several potential attack scenarios and approaches event encryption from the perspective of neuromorphic noise removal, in which we inversely introduce well-crafted noise into raw events until they are obfuscated. Evaluations show that the encrypted events can effectively protect information from the attacks of low-level visual reconstruction and high-level neuromorphic reasoning, and thus feature dependable privacy-preserving competence. Our solution gives impetus to the security of event data and paves the way to a highly encrypted technique for privacy-protective neuromorphic imaging