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.

翻译：多式助听器(HAs)旨在通过背景感测和处理数据,不仅以音频信息,而且以视觉信息(如唇读)的形式,在吵闹的环境中提供更易理解的听力(HAs),目的是通过背景感测和处理数据,不仅以视听信息(如唇读)的形式,在声音噪音环境中提供更易理解的听力(HAs),机械学习技术可以对多式数据的背景处理发挥关键作用,然而,由于HA装置的计算功率较低,这些数据必须在边缘或云层上处理,这反过来又对敏感的用户数据造成隐私问题。现有文献提出了数据加密的若干技术,但其计算复杂性是一个重大瓶颈,可满足开发未来多式助听器的严格耐久要求。为解决这一问题,本文提议采用基于混乱性加密的实时视听数据加密方案,利用Tangent-Delay Ellipse Remainal Remaculation Cavity-Map系统(TD-ERCS)地图和非线性查托(NCA)图(NCA)图中的拟议安全性高度(即比比比前的测试性、比比比比前的测试性、比比前的系统)分析、更低性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、比性、性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性性