1. Introduction

In the era of being fully swept by the wave of digitalization, the way of information transmission and communication has undergone a radical change. As the most natural and direct form of human communication since ancient times, the voice, supported by digital technology, is experiencing unprecedented innovations and breakthroughs in the way it is captured, transmitted and stored. Whether it is daily telephone conversations between people, remote meetings across geographical restrictions, or convenient voice command control in smart home environments, or even the wide dissemination of music works on digital platforms, voice data has been deeply integrated into every aspect of our lives, becoming an indispensable and key component of the digital realm [1].

However, with the widespread application and popularization of voice data in various fields, a series of worrying problems have gradually surfaced. Among them, the most prominent is the security and privacy of voice data. While people are enjoying the convenience and efficiency brought by voice technology, they are also beginning to pay more and more attention to the risk of leakage of personal voice information in the process of collection, transmission and storage, as well as the potential threat that these risks may pose to the rights and interests of individuals and social order. Because of this, an in-depth discussion on how to protect the security and privacy of voice data has become an important topic that needs to be solved. This paper will focus on the innovative application of artificial intelligence (AI) in the field of voice encryption, aiming to build a solid, efficient and secure protective barrier for the transmission and storage of voice data.

2. Applications and challenges of voice data

2.1. Applications

In daily life, telephone conversation is still one of the important ways for people to communicate remotely. Voice data is rapidly transmitted through telephone or network, which enables people in different places to exchange emotions, share information and solve problems in real time [2]. Whether it is the cordial greeting between friends and relatives, or the work communication between business partners, the voice data in telephone conversation carries rich emotions and important information.

In the work environment, teleconferencing is gradually becoming a common mode of collaboration within organizations such as enterprises and academic institutions. Participants use voice to conduct real-time discussions, decision-making and reporting through an online platform. This type of voice communication across spatial constraints greatly improves work efficiency and promotes teamwork, but it also means that a large amount of sensitive information is transmitted through voice data in the network [3].

In the field of music, the popularization of digital technology has led to a fundamental change in the way musical works are disseminated. Voice elements such as singers' voices and instrumental performances are widely disseminated in digital form on the network, which satisfies the needs of the majority of music lovers [2]. However, at the same time, voice data in musical works face challenges of copyright protection and illegal distribution.

In addition, the rise of the smart home has brought an unprecedentedly convenient experience to people's lives. Through simple voice commands, people can easily control the lights, temperature, home appliances and other devices in their homes. In this process, voice data is captured and analyzed for accurate understanding and response to user commands [2]. However, this also means that voice information in the home environment may be stored and transmitted to the cloud, which poses certain security risks.

2.2. Challenges

In the data collection segment, various voice collection devices and applications may over-collect voice information without the user's knowledge. For example, certain smartphone applications may continuously listen to the user's voice in the background to obtain the user's preferences and behavioral patterns, and this unauthorized collection behavior seriously infringes on the user's privacy.

As well, during the transmission of voice data, the data can be easily intercepted, tampered or stolen by hackers due to the complexity and openness of the network environment. Especially in the wireless network environment, imperfect encryption measures may result in voice data being exposed during transmission, bringing great security risks to users [3].

Female In addition, in terms of storage, a large amount of voice data is stored in cloud servers or enterprise databases. If the security protection measures of these storage systems are not strong enough, they may be attacked by hackers, resulting in voice data leakage. In addition, irregular operation or improper data management by internal personnel may also cause accidental leakage of voice data.

3. Innovative Applications of AI in Voice Encryption

3.1. Intelligent Adaptive Encryption Technology

In the field of speech encryption, intelligent adaptive encryption technology is a revolutionary advancement. This technology is able to dynamically adjust the encryption parameters according to the characteristics of the input speech signal and the surrounding environment, as well as intelligently adjusting the complexity of the encryption algorithm according to factors such as the sensitivity of the speech content and the security requirements of the transmission environment, etc., and taking into account the speaker's intonation, timbre, and speed of speech, as well as factors such as the noise level and the conditions of the transmission channel in the judgment [4-5]. By intelligently adapting to these variables, the encryption process becomes more precise and secure, effectively preventing unauthorized access and decryption attempts. This adaptive encryption not only effectively protects sensitive information, but also optimizes encryption efficiency and reduces resource consumption while ensuring security.

3.2. Deep Learning-Based Voice Feature Extraction and Encryption

The application of deep learning in speech feature extraction and encryption greatly improves the security and efficiency of speech encryption. By utilizing deep neural networks, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), complex and unique high-dimensional features of speech signals can be extracted from the original data [6]. These features are then encrypted using advanced encryption algorithms to ensure the confidentiality and integrity of the speech data. This approach reduces the amount of encrypted data, improves encryption efficiency, and enhances robustness, ensuring that even modified or compressed encrypted speech maintains high decryption quality [7]. In addition, deep learning models can learn patterns and variations in speech, leading to more efficient encryption and enhanced protection against potential attacks.

3.3. Voiceprint Recognition and Personalized Encryption

Voiceprint recognition technology plays a crucial role in personalized speech encryption. Voiceprint recognition technology uses artificial intelligence to analyze unique biometric features in an individual's voiceprint, such as the shape of the vocal tract and specific patterns of vocal fold vibration, which can generate personalized encryption keys for personal authentication [5]. This ensures that only authorized users with matching voiceprints can decrypt and access encrypted speech. Applying this technique to voice encryption can facilitate personalized encryption strategies. This personalized approach adds an extra layer of security that makes it much more difficult for unauthorized individuals to access encrypted information. This personalized approach not only strengthens security but also enhances user experience [7].

3.4. Reversible Watermarking Technology for Encrypted Voices

By combining artificial intelligence with digital watermarking technology, reversible watermarks can be embedded in encrypted speech data for purposes such as copyright protection and content tracking.

Embedding additional information such as digital signatures or timestamps in encrypted speech data in a reversible manner can verify the origin and integrity of the speech [5]. These watermarks have no effect on the sound quality and can be accurately extracted and verified during the decryption process, providing strong support for the legitimate use of speech and rights protection. In ensuring the confidentiality of voice content, it also provides a means of detecting any tampering or unauthorized modifications. And the reversibility of the watermark ensures that the original voice data can be recovered without any loss of quality after the verification process is complete [7]. Reversible watermarking technology provides a new solution for protecting the integrity and authenticity of encrypted speech.

4. Related research

4.1. Security and Privacy of Artificial Intelligence Models

Artificial intelligence models may be subject to attacks during training and inference, such as model stealing attacks, model reversal attacks, and membership inference attacks. Therefore, the current research mainly involves three perspectives of data security, model security, and model privacy, and explores the protection of AI models from adversarial attacks while ensuring data privacy.

In addition, in the field of AI full life cycle privacy risk analysis, the privacy risks that AI may encounter during the full life cycle of data collection, storage, usage sharing, model training, and model inference application are analyzed, and effective privacy protection measures are explored, including permission management of the data, classification and hierarchical protection, as well as comprehensive monitoring and control of all stages in the data life cycle [8].

In the field of artificial intelligence, adversarial attacks are methods that mislead machine learning models to make wrong judgments through carefully designed inputs. Defense mechanisms, on the other hand, aim to improve the robustness of the model and reduce the impact of adversarial attacks. Research in this area includes the generation and defense techniques of adversarial samples, as well as the application of adversarial attacks in different fields, such as computer vision and natural language processing [9].

For the AI model training and inference phases, it is proposed to counter member inference attacks, model reversal attacks, model extraction attacks, etc. according to different attack types to reduce the privacy risk due to model leakage.

Researchers are developing multi-dimensional data security assessment models are also currently under development to facilitate quantitative assessment and effectiveness testing of data security [10]. This includes multiple dimensions of assessment such as leakage prevention coverage indicators, data security incident handling, and data security emergency response [10]. The development and assessment of the effectiveness of privacy protection technologies in practical applications, such as differential privacy, homomorphic encryption, and multi-party secure computing, can protect the security and compliance of the platform.

4.2. Smart home

4.2.1. Huawei HiLink. Huawei HiLink is a smart home ecosystem that supports the connection of a variety of smart devices, such as smart bulbs, sockets, speakers, etc., via Wi-Fi and provides voice control. The security of Huawei HiLink is reflected in its comprehensive technical protection of device anti-counterfeiting security mechanism, APP anti-cracking, WAN encrypted transmission and LAN rights management. This ensures that users' voice information is protected during use [11].

The Huawei HiLink platform supports two access methods: direct hardware connection and cloud-cloud docking. Whether it is direct device connection or cloud-cloud docking, Huawei emphasizes the security of data transmission and uses end-to-end encryption technology to protect the confidentiality of voice data during storage and transmission [12]. In communication scenarios, Huawei utilizes its hardware and software advantages in communications to ensure data integrity and confidentiality, providing users with a safe and secure environment for using AI speakers [11]. Huawei's AI speaker also ensures the security of data in different usage scenarios through multi-level deep protection skills, including data encryption, access control, and other security measures.

In addition, Huawei has established a privacy compliance baseline that contains a collection of privacy control measures, which in turn covers the full lifecycle of customers' personal information. Huawei's terminal business always adheres to the design principles of personal information minimization, end-side processing of personal information, and user transparency and control, and has introduced a variety of privacy features, such as the Privacy Platform, Security Center, Image Privacy Protection, and AI Privacy Protection [12]. The data minimization principle is to collect only the relevant data necessary to provide the service, and to use encryption measures when collecting, processing, and transmitting this data to reduce the risk of data leakage [11]. Users' critical privacy data, such as voiceprint data, is usually processed on the device side and not uploaded to the cloud, which further protects users' privacy and security. User transparency means that Huawei clearly informs users of the purpose of data collection and how it will be used before they use the Smart Voice service, and handles personal information after obtaining their consent, ensuring that users have a clear understanding of how their personal information will be used, and that they can independently make independent decisions about the use of the information.

As well, Huawei uses random identifiers rather than user IDs to associate personal information with users, so that even if data is sent to a remote server, it is not associated with the user [12]. In addition, Huawei uses differential privacy technology to protect the user's identity by generating a summary of the raw data and adding random noise so that this data cannot be associated with the user.

4.2.2. Josh.ai. Josh.ai is a company that specializes in high-end smart home systems, and its products focus on managing various devices and systems in the home, such as lights, stereos, TVs, curtains, air conditioners, etc., through voice control [13]. When it comes to protecting the security of user voice information, Josh.ai pays close attention to user privacy and security, and uses end-to-end encryption technology to protect user voice data from being stolen or misused. This encryption ensures that only the user and the intended recipient can decrypt and access the voice data, even if the data is intercepted in transit, and cannot be decoded by a third party. Smart home systems are controlled through intelligent voice control, allowing users to control the automation systems in their homes through voice commands [14]. This interaction is smarter and more natural, while ensuring the security and privacy of user data. At the same time, Josh.ai offers a smart home system that can provide personalized answers based on the location of the home and family traits, while focusing on protecting the user's privacy and data security in the delivery of its services.

In addition, Josh.ai is currently working with Amazon to further develop the multi-assistant integration potential of the home smart system. This means that when the Josh.ai assistant controls a smart home or connected device, users will be able to use Alexa to handle a variety of voice requests at the same time, an integration that not only improves convenience but also enhances data security [13].

5. Challenges and Outlook

With the continuous development and innovation of artificial intelligence technology, it is expected that voice encryption technology will become more intelligent, personalized and integrated in the future. For example, by combining with blockchain technology, more decentralized and tamper-proof storage and transmission of voice data can be achieved; the strength and efficiency of encryption algorithms can be further improved by taking advantage of quantum computing.

However, despite the great potential of AI in the field of voice encryption, it still faces many challenges. First, ensuring the security and stability of encryption algorithms to prevent cracking or attacks is the top priority. Second, with the exponential growth of speech data, it is critical to improve encryption efficiency while maintaining effectiveness and reducing computational costs. In addition, striking a balance between the complexity of encryption and the convenience of user experience is also an important future research direction.

6. Conclusion

Today, with the wave of digitization sweeping across the world, voice, as one of the most natural and direct means of communication for human beings, is experiencing unprecedented innovations and breakthroughs in the way it is captured, transmitted, and stored under the auspices of digital technology. Speech data has been deeply integrated into every aspect of our lives and has become an indispensable and key component of the digital domain.

However, while enjoying the convenience and efficiency brought by voice technology, people have begun to pay more and more attention to the risks of leakage of personal voice information during the collection, transmission, and storage of voice data, as well as the potential threats that these risks may pose to the rights and interests of individuals and social order.

In the field of voice encryption, innovative applications of Artificial Intelligence (AI) are building a solid, efficient and secure protective barrier to protect the transmission and storage of voice data. This includes intelligent adaptive encryption, deep learning-based speech feature extraction and encryption, voiceprint recognition and personalized encryption, and reversible watermarking technologies. These technologies can dynamically adjust encryption parameters, extract complex and unique features of speech signals, generate personalized encryption keys, and embed reversible watermarks in encrypted speech data for copyright protection and content tracking.

However, the need to ensure the security and stability of the encryption algorithms, improve the encryption efficiency, maintain the effectiveness and reduce the computational cost also arises. Therefore, how to strike a balance between encryption complexity and user experience convenience is also an important future research direction.

In this paper, we just analyze the encryption of speech information from the perspective of literature review, and will subsequently conduct relevant experimental demonstration on this basis to promote the research process of artificial intelligence on speech recognition and protection.