中文标题#
一種隱私保護語言建模方法的研究
英文标题#
A Study of Privacy-preserving Language Modeling Approaches
中文摘要#
最近在語言建模方面的進展增加了它們在各種應用和領域中的使用。 語言模型通常在敏感數據上進行訓練，可能在隱私攻擊期間記住並洩露這些信息，引發了對保護個人隱私權的關注。 在語言模型中保護隱私已成為一個關鍵的研究領域，因為隱私是基本的人類權利之一。 儘管其重要性，人們對這些語言模型所具有的隱私風險程度以及如何減輕風險的理解仍然有限。 本研究通過提供對隱私保護語言建模方法的全面研究來解決這個問題。 本研究對這些方法進行了深入概述，強調了它們的優勢，並探討了它們的局限性。 本研究的結果有助於隱私保護語言建模的持續研究，提供了有價值的見解並指明了未來的研究方向。
英文摘要#
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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