Differentially-Private Text Rewriting reshapes Linguistic Style

📅 2026-04-29
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🤖 AI Summary
This study addresses the underexplored impact of differentially private text rewriting on stylistic register, despite its aim to preserve semantic content. By conducting multidimensional register analysis and employing both autoregressive paraphrasing and bidirectional substitution methods for sentence-level differential privacy under varying privacy budgets, the work reveals that privacy constraints systematically reduce interactional markers, contextual references, and complex subordinate clauses. Consequently, privatized texts gravitate toward a homogeneous, non-interactive, and non-persuasive register, thereby diminishing stylistic nuance. This paper is the first to demonstrate the systematic influence of differential privacy mechanisms on textual communicative functions, highlighting the inherent tension between privacy preservation and stylistic fidelity.
📝 Abstract
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.
Problem

Research questions and friction points this paper is trying to address.

Differential Privacy
Text Rewriting
Linguistic Style
Register Identity
Stylistic Homogenization
Innovation

Methods, ideas, or system contributions that make the work stand out.

Differential Privacy
Text Rewriting
Stylistic Profiling
Register Identity
Language Models
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S
Stefan Arnold
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany