梳理移动端大模型的隐私安全挑战与防护方案
A Survey: Towards Privacy and Security in Mobile Large Language Models
- 系统分类隐私保护技术如差分隐私、联邦学习
- 揭示移动端特有攻击漏洞并评估防御效果
- 适合关注移动AI安全的研究者与开发者
移动大型语言模型（LLMs）凭借在医疗、金融、教育等领域的强大自然语言处理能力，正推动智能化应用向终端延伸。然而，其在移动和边缘环境部署时，因资源消耗大及处理敏感数据，面临严峻的隐私与安全挑战。本综述全面梳理了与移动LLMs相关的隐私安全问题，系统性地归类现有解决方案，包括差分隐私、联邦学习和提示加密等。同时，深入分析移动端特有的漏洞，如对抗攻击、成员推断攻击和侧信道攻击，并对比各类方法的有效性与局限性。尽管已有进展，移动LLMs在资源受限环境下实现强安全与高效率仍存障碍。本文提出潜在应用场景，讨论开放挑战，并建议未来研究方向，旨在推动可信赖、合规且可扩展的移动LLM系统发展。
原文摘要 · Abstract (English)
Mobile Large Language Models (LLMs) are revolutionizing diverse fields such as healthcare, finance, and education with their ability to perform advanced natural language processing tasks on-the-go. However, the deployment of these models in mobile and edge environments introduces significant challenges related to privacy and security due to their resource-intensive nature and the sensitivity of the data they process. This survey provides a comprehensive overview of privacy and security issues associated with mobile LLMs, systematically categorizing existing solutions such as differential privacy, federated learning, and prompt encryption. Furthermore, we analyze vulnerabilities unique to mobile LLMs, including adversarial attacks, membership inference, and side-channel attacks, offering an in-depth comparison of their effectiveness and limitations. Despite recent advancements, mobile LLMs face unique hurdles in achieving robust security while maintaining efficiency in resource-constrained environments. To bridge this gap, we propose potential applications, discuss open challenges, and suggest future research directions, paving the way for the development of trustworthy, privacy-compliant, and scalable mobile LLM systems.
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