Authorship identification has proven unsettlingly effective in inferring the identity of the author of an unsigned document, even when sensitive personal information has been carefully omitted. In the digital era, individuals leave a lasting digital footprint through their written content, whether it is posted on social media, stored on their employer's computers, or located elsewhere. When individuals need to communicate publicly yet wish to remain anonymous, there is little available to protect them from unwanted authorship identification. This unprecedented threat to privacy is evident in scenarios such as whistle-blowing. Proposed defenses against authorship identification attacks primarily aim to obfuscate one's writing style, thereby making it unlinkable to their pre-existing writing, while concurrently preserving the original meaning and grammatical integrity. The presented work offers a comprehensive review of the advancements in this research area spanning over the past two decades and beyond. It emphasizes the methodological frameworks of modification and generation-based strategies devised to evade authorship identification attacks, highlighting joint efforts from the differential privacy community. Limitations of current research are discussed, with a spotlight on open challenges and potential research avenues.

翻译：作者身份识别已被证明在推断无署名文档作者身份方面异常有效——即使敏感个人信息已被仔细隐去。在数字时代，个人通过其撰写的内容（无论是发布于社交媒体、存储于雇主电脑还是其他位置）留下持久的数字足迹。当个人需要公开交流却希望保持匿名时，几乎没有什么方法能保护其免受不期望的作者身份识别侵害。这种空前的隐私威胁在举报等场景中尤为明显。针对作者身份识别攻击提出的防御措施主要旨在混淆个人写作风格，使其与既有作品无法关联，同时保留原始语义与语法完整性。本文对过去二十余年间该研究领域的进展进行了全面综述，重点阐述了基于修改与生成的规避作者身份识别攻击策略的方法框架，并强调差分隐私社区的协同贡献。本文还讨论了当前研究的局限性，着重指出了开放挑战与潜在研究方向。