  Multimodal hearing aids (HAs) aim to deliver more intelligible audio in noisy
environments by contextually sensing and processing data in the form of not
only audio but also visual information (e.g. lip reading). Machine learning
techniques can play a pivotal role for the contextually processing of
multimodal data. However, since the computational power of HA devices is low,
therefore this data must be processed either on the edge or cloud which, in
turn, poses privacy concerns for sensitive user data. Existing literature
proposes several techniques for data encryption but their computational
complexity is a major bottleneck to meet strict latency requirements for
development of future multi-modal hearing aids. To overcome this problem, this
paper proposes a novel real-time audio/visual data encryption scheme based on
chaos-based encryption using the Tangent-Delay Ellipse Reflecting Cavity-Map
System (TD-ERCS) map and Non-linear Chaotic (NCA) Algorithm. The results
achieved against different security parameters, including Correlation
Coefficient, Unified Averaged Changed Intensity (UACI), Key Sensitivity
Analysis, Number of Changing Pixel Rate (NPCR), Mean-Square Error (MSE), Peak
Signal to Noise Ratio (PSNR), Entropy test, and Chi-test, indicate that the
newly proposed scheme is more lightweight due to its lower execution time as
compared to existing schemes and more secure due to increased key-space against
modern brute-force attacks.

Cryptography and Security;Sound;Audio and Speech Processing