系统分析语言模型隐私风险与防护方法
A Study of Privacy-preserving Language Modeling Approaches
- 梳理主流隐私保护语言建模技术及其原理
- 揭示现有方法在隐私泄露防御上的局限性
- 为研究者提供未来方向与实用参考
近年来，语言模型在各领域应用日益广泛。由于通常基于敏感数据训练，这些模型可能在隐私攻击下泄露记忆信息，引发对个人隐私权的担忧。保护语言模型中的隐私已成为关键研究课题，因为隐私是基本人权之一。尽管重要性凸显，当前对语言模型隐私风险程度及缓解方法的理解仍有限。本文通过全面研究隐私保护语言建模方法，深入剖析各类技术，指出其优势与不足。研究成果有助于推进该领域发展，提供宝贵洞见并明确未来研究方向。
原文摘要 · Abstract (English)
Recent developments in language modeling have increased their use in various applications and domains. Language models, often trained on sensitive data, can memorize and disclose this information during privacy attacks, raising concerns about protecting individuals' privacy rights. Preserving privacy in language models has become a crucial area of research, as privacy is one of the fundamental human rights. Despite its significance, understanding of how much privacy risk these language models possess and how it can be mitigated is still limited. This research addresses this by providing a comprehensive study of the privacy-preserving language modeling approaches. This study gives an in-depth overview of these approaches, highlights their strengths, and investigates their limitations. The outcomes of this study contribute to the ongoing research on privacy-preserving language modeling, providing valuable insights and outlining future research directions.
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