Differentially-Private Text Rewriting reshapes Linguistic Style

Problem
This paper addresses the gap in understanding the impact of Differential Privacy (DP) on the linguistic style of text during the rewriting process. While previous work has focused on word-level substitutions, this study explores the implications of sentence-level rewriting on the communicative signature of text. The authors highlight that the effects of privacy constraints on stylistic elements have not been thoroughly investigated, which is critical for applications requiring both privacy and coherent text generation. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors employ a comparative analysis of two architectures for text rewriting: autoregressive paraphrasing and bidirectional substitution. They utilize a range of privacy budgets to assess the stylistic impact of differentially-private rewriting. The core technical contribution lies in the multidimensional stylistic profiling approach, which quantifies changes in interactive markers, contextual references, and complex subordination in the rewritten text. The study leverages state-of-the-art language models to generate text while adhering to DP constraints, allowing for a systematic evaluation of how privacy affects linguistic style.

Results
The findings reveal that both rewriting methods lead to a significant convergence towards a non-involved and non-persuasive register, regardless of the privacy budget employed. Specifically, the authors report a marked reduction in interactive markers and contextual references, with complex subordination structures being notably diminished. The results indicate that while semantic content is preserved, the stylistic nuances that characterize human-authored discourse are systematically homogenized. The paper does not provide specific quantitative metrics or effect sizes against named baselines, which could enhance the clarity of the results.

Limitations
The authors acknowledge that their analysis is limited to the stylistic aspects of text rewriting under DP constraints and does not explore the broader implications for content accuracy or user intent. Additionally, the study does not consider the potential trade-offs between privacy and other dimensions of text quality, such as creativity or emotional resonance. An obvious limitation not flagged by the authors is the lack of a comprehensive evaluation across diverse datasets, which may affect the generalizability of the findings.

Why it matters
This research has significant implications for the development of privacy-preserving text generation systems, particularly in contexts where maintaining stylistic integrity is crucial, such as in creative writing or personalized communication. The insights gained from this study can inform future work on balancing privacy guarantees with the preservation of linguistic style, potentially leading to improved methodologies for differentially-private text generation. Understanding the stylistic trade-offs involved in DP can also guide the design of more sophisticated models that better retain the nuances of human language while ensuring user privacy.

Authors: Stefan Arnold
Source: arXiv:2604.26656
URL: https://arxiv.org/abs/2604.26656v1