Trustworthy AI-Generative Content in Intelligent 6G Network: Adversarial, Privacy, and Fairness
<|reference_start|>Trustworthy AI-Generative Content in Intelligent 6G Network: Adversarial, Privacy, and Fairness: AI-generated content (AIGC) models, represented by large language models (LLM), have brought revolutionary changes to the content generation fields. The high-speed and extensive 6G technology is an ideal platform for providing powerful AIGC mobile service applications, while future 6G mobile networks also need to support intelligent and personalized mobile generation services. However, the significant ethical and security issues of current AIGC models, such as adversarial attacks, privacy, and fairness, greatly affect the credibility of 6G intelligent networks, especially in ensuring secure, private, and fair AIGC applications. In this paper, we propose TrustGAIN, a novel paradigm for trustworthy AIGC in 6G networks, to ensure trustworthy large-scale AIGC services in future 6G networks. We first discuss the adversarial attacks and privacy threats faced by AIGC systems in 6G networks, as well as the corresponding protection issues. Subsequently, we emphasize the importance of ensuring the unbiasedness and fairness of the mobile generative service in future intelligent networks. In particular, we conduct a use case to demonstrate that TrustGAIN can effectively guide the resistance against malicious or generated false information. We believe that TrustGAIN is a necessary paradigm for intelligent and trustworthy 6G networks to support AIGC services, ensuring the security, privacy, and fairness of AIGC network services.<|reference_end|>
@article{li2024trustworthy,
    title={Trustworthy AI-Generative Content in Intelligent 6G Network:
  Adversarial, Privacy, and Fairness},
    author={Siyuan Li, Xi Lin, Yaju Liu, Jianhua Li},
    journal={arXiv preprint arXiv:2405.05930},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05930},
    primaryClass={cs.CR cs.AI cs.NI}
}