  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 harsh
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 pose serious security threats.
Therefore, asynchronous event streams necessitate careful encryption before
transmission and usage. This work 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. Our evaluations show that the encrypted events can
effectively protect information from attacks of low-level visual reconstruction
and high-level neuromorphic reasoning, and thus feature dependable
privacy-preserving competence. The proposed solution gives impetus to the
security of event data and paves the way to a highly encrypted technique for
privacy-protective neuromorphic imaging.
