差分隐私文本重写重塑语言风格
Differentially-Private Text Rewriting reshapes Linguistic Style
AI总结：
研究探讨差分隐私文本重写对语言风格的影响，发现隐私保护导致文本功能特征系统性改变，削弱互动标记和复杂从属结构，使文本风格趋于非参与和非说服性。
摘要：
差分隐私（DP）通过利用语言模型的生成能力，使文本隐私化从分散的词级替换发展到连续的句级重写。尽管这种形式的文本隐私化在平衡正式隐私保证与语法连贯性方面表现最佳，但其对文本语域身份的影响仍鲜有探索。通过多维风格分析，研究发现隐私保护的成本远超词汇变化。具体而言，隐私约束下的重写导致文本沟通特征系统性改变，表现为互动标记、上下文参考和复杂从属结构的严重退化。通过比较自回归改写与双向替换在不同隐私预算下的表现，发现两种架构均迫使文本向非参与、非说服性的语域收敛。这种语域盲的净化有效保留了语义内容，但结构上使定义人类写作的细腻风格标记趋于同质化。
英文摘要：
Differential Privacy (DP) for text matured from disjointed word-level substitutions to contiguous sentence-level rewriting by leveraging the generative capacity of language models. While this form of text privatization is best suited for balancing formal privacy guarantees with grammatical coherence, its impact on the register identity of text remains largely unexplored. By conducting a multidimensional stylistic profiling of differentially-private rewriting, we demonstrate that the cost of privacy extends far beyond lexical variation. Specifically, we find that rewriting under privacy constraints induces a systematic functional mutation of the text's communicative signature. This shift is characterized by the severe attrition of interactive markers, contextual references, and complex subordination. By comparing autoregressive paraphrasing against bidirectional substitution across a spectrum of privacy budgets, we observe that both architectures force convergence toward a non-involved and non-persuasive register. This register-blind sanitization effectively preserves semantic content but structurally homogenizes the nuanced stylistic markers that define human-authored discourse.
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