Event Encryption: Rethinking Privacy Exposure for Neuromorphic Imaging

TL;DR

This paper proposes a novel event encryption method for neuromorphic cameras that effectively protects sensitive visual information from reconstruction and reasoning attacks, enhancing privacy in neuromorphic imaging systems.

Contribution

It introduces a noise-based event encryption approach tailored for neuromorphic cameras, addressing privacy concerns with a method that obfuscates raw event data against various attack scenarios.

Findings

Encrypted events resist visual reconstruction attacks

Encrypted events prevent neuromorphic reasoning attacks

The method ensures reliable privacy protection for neuromorphic data

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 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.