arXiv:2604. 07486v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have emerged as a powerful tool for synthetic data generation.
By Qian Ma, Sarah Rajtmajer
The paper introduces a style-aware paraphrasing method for text anonymization that leverages pretrained large language models to build compact stylistic profiles from minimal samples and rewrite text to suppress identifiable style markers while preserving meaning. It demonstrates that this approach reduces authorship attribution F1 scores by 60‑70% on blog and review datasets, outperforming both differential privacy‑based and non‑DP baselines, and maintains content quality and readability.
By Ahmed Sohair Khan, Estrid He, Monica Wachowicz, Elham Naghizade
DP-IPI introduces a hybrid differential privacy text rewriting mechanism that selectively privatizes only the spans containing indirect personal identifiers (IPIs) in clinical texts. By targeting these specific tokens rather than all words, the method preserves higher text quality and usability while still reducing re-identification risks. The approach demonstrates improved privacy‑utility trade‑offs compared to indiscriminate DP text rewriting techniques.
By Ibrahim Baroud, Stephen Meisenbacher, Sebastian M\"oller, Florian Matthes, Roland Roller
arXiv:2608.29624v1 Announce Type: new
Abstract: Natural Language Processing methods have enabled novel solutions and advances in the field of privacy, particularly in the sub-domain of text-to-text p...
By Stephen Meisenbacher, Andreea-Elena Bodea, Ahmet Bilal Ak{\i}n, Alexandra Klymenko, Jana Diesner, Florian Matthes
The paper introduces the privacy‑HSD trade‑off, highlighting that automatic hate speech detection systems can inadvertently compromise user privacy by encoding authorship. It demonstrates that such systems may achieve high performance at the expense of privacy, and proposes a new domain‑specific technique, AgnoSpeech, alongside other text privatization methods to balance these competing goals. The authors benchmark these methods, showing that while challenging, it is feasible to protect privacy without sacrificing hate‑speech detection effectiveness.
Redakto is a new tool designed to anonymize text before it is processed by large language models (LLMs). It offers state‑of‑the‑art redaction of personally identifiable information (PII) and pseudonymization, accessible via a web interface, REST APIs, and model context protocol hooks. The authors evaluate its performance on legal and medical datasets, showing that anonymized texts retain utility comparable to the originals, enabling LLM tasks without significant loss of effectiveness.
By Saurav Kumar Saha, Tom R\"ohr, Felix Bie{\ss}mann