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Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation.

Bowen Du¹, Weiqi Li², Zeju Wang²

¹Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

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This study introduces the first encryption framework for Neuromorphic Vision Sensors (NVS). The proposed method effectively protects sensitive NVS data against reconstruction and identification attacks while maintaining high efficiency.

Area of Science:

Computer Vision
Cybersecurity
Sensor Technology

Background:

Vision-based applications like surveillance and identification are widespread.
Neuromorphic Vision Sensors (NVS) offer reduced latency but pose unique privacy challenges.
Existing encryption methods are not designed for the event-based data stream of NVS.

Purpose of the Study:

To address the significant privacy challenges associated with Neuromorphic Vision Sensors (NVS).
To develop and evaluate a novel encryption framework specifically for NVS event data.
To protect privacy-preserving information within NVS data streams from unauthorized access and reconstruction.

Main Methods:

Analysis of potential security attacks on NVS, including image reconstruction and classification.

Keywords:

event camera event encryption neuromorphic vision sensor privacy security

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Proposal of a dedicated encryption framework utilizing 2D chaotic mapping for event scrambling and polarity flipping.

Implementation of an updating score mechanism for efficient, platform-adaptive encryption.

Main Results:

Demonstrated effective protection against grayscale image reconstruction from NVS events.
Showcased successful defense against privacy-related human identification using NVS data.
Achieved high encryption efficiency across various platforms, including resource-constrained devices.

Conclusions:

The proposed encryption framework is the first to specifically secure Neuromorphic Vision Sensor (NVS) data.
The framework effectively mitigates privacy risks from image reconstruction and identification attacks.
The solution offers efficient and adaptable privacy protection for NVS applications.