The increasing reliance on voice communication in various industries highlights the importance of maintaining privacy and security. Artificial Intelligence (AI) has the potential to transform the landscape of secure voice technology, offering innovative solutions to protect voice communications from cyber threats (Huang et al., 2019).
AI-driven encryption methods leverage machine learning algorithms to enhance the security of voice communication. Techniques such as neural cryptography and homomorphic encryption enable secure data transmission, even in the presence of eavesdroppers, by making it computationally infeasible to decrypt intercepted messages without the correct keys (Abadi & Andersen, 2016).
AI algorithms can also be employed to monitor voice communication networks for unusual patterns or anomalies, indicating potential security breaches. By training machine learning models on large datasets of voice traffic, these systems can identify and prevent unauthorized access or tampering with voice communications (Yadav & Selvakumar, 2018).
Voice assistants, such as Amazon's Alexa and Apple's Siri, have become increasingly popular in recent years. AI can enhance the security of these voice assistants by implementing end-to-end encryption, secure authentication, and context-aware privacy controls, ensuring that sensitive user data remains protected (Kumar et al., 2021).
While AI offers numerous benefits for secure voice technology, it also raises several ethical concerns and potential risks:
Privacy: AI algorithms may inadvertently compromise user privacy by analyzing and storing sensitive voice data.
Bias: Machine learning models trained on biased datasets may introduce unintentional discrimination in AI-driven secure voice systems.
Misuse: Malicious actors could exploit AI technologies to develop sophisticated attacks on voice communication systems.
Artificial Intelligence has the potential to revolutionize secure voice technology, offering innovative solutions for encryption, anomaly detection, and secure voice assistants. However, the ethical considerations and potential risks associated with AI must be carefully addressed to ensure a secure and equitable future for voice communication.
Abadi, M., & Andersen, D. G. (2016). Learning to protect communications with adversarial neural cryptography. In Proceedings of the 30th International Conference on Neural Information Processing Systems (pp. 1-9).
Huang, Q., Yang, Y., & Metaxas, D. (2019). Deep learning for secure mobile edge computing. IEEE Communications Magazine, 57(10), 84-89.
Kumar, N., Zeadally, S., & Salahuddin, M. A. (2021). Privacy-preserving solutions for voice assistants. IEEE Consumer Electronics Magazine, 10(2), 87-95.
Yadav, A., & Selvakumar, S. (2018). Machine learning-based intrusion detection for IoT. In S. Selvakumar & A. Yadav (Eds.), Secure Cyber-Physical Systems for IoT (pp. 29-43). Springer
