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