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Existing audio-only hearing-aids are known to perform poorly in noisysituations where overwhelming noise is present. Next-generation audio-visual(lip-reading driven) hearing-aids stand as a major enabler to realise moreintelligible audio. However, high data rate, low latency, low computationalcomplexity, and privacy are some of the major bottlenecks to the successfuldeployment of such advanced hearing aids. To address these challenges, weenvision an integration of 5G Cloud-Radio Access Network, Internet of Things(IoT), and strong privacy algorithms to fully benefit from the possibilitiesthese technologies have to offer. The envisioned 5G IoT enabled secureaudio-visual (AV) hearing-aid transmits the encrypted compressed AV informationand receives encrypted enhanced reconstructed speech in real-time which fullyaddresses cybersecurity attacks such as location privacy and eavesdropping. Forsecurity implementation, a real-time lightweight AV encryption is utilized. Forspeech enhancement, the received AV information in the cloud is used to filternoisy audio using both deep learning and analytical acoustic modelling(filtering based approach). To offload the computational complexity andreal-time optimization issues, the framework runs deep learning and big dataoptimization processes in the background on the cloud. Specifically, in thiswork, three key contributions are reported: (1) 5G IoT enabled secureaudio-visual hearing-aid framework that aims to achieve a round-trip latency upto 5ms with 100 Mbps datarate (2) Real-time lightweight audio-visual encryption(3) Lip-reading driven deep learning approach for speech enhancement in thecloud. The critical analysis in terms of both speech enhancement and AVencryption demonstrate the potential of the envisioned technology in acquiringhigh-quality speech reconstruction and secure mobile AV hearing aidcommunication.

Real-Time Lightweight Chaotic Encryption for 5G IoT Enabled Lip-Reading Driven Secure Hearing-Aid