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
arXiv:2601. 04641v2 Announce Type: replace-cross Abstract: The deployment of Machine-Generated Text (MGT) detection systems necessitates processing sensitive user data, creating a fundamental conflict between authorship verification and privacy preservation.
By Lionel Z. Wang, Yusheng Zhao, Jiabin Luo, Xinfeng Li, Lixu Wang, Yinan Peng, Haoyang Li, XiaoFeng Wang, Wei Dong
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.
arXiv:2610.27259v1 Announce Type: cross
Abstract: The rapid evolution of Large Language Models is fueled by massive datasets, yet this reliance poses severe privacy risks, as models can inadvertently...
By Qi Pang, Hanshen Xiao, Andre Manoel, Lipika Ramaswamy, Wenting Zheng, Virginia Smith, G. Edward Suh
arXiv:2606. 24623v1 Announce Type: cross Abstract: Retrieval-Augmented Generation enhances large language models by incorporating external knowledge, but deploying it in sensitive scenarios risks privacy leakage via malicious prompts.
By Yuanhe Zhao, Tianyu Zhang, Huafei Xing, Derek F. Wong, Jianbin Li, Tao Fang
The paper introduces a lightweight privacy-preserving technique for Retrieval-Augmented Generation (RAG) systems that uses random projection to transform both datastore and query embeddings. This approach maintains semantic similarity for effective retrieval while significantly reducing the risk of data extraction attacks. Experiments across various RAG architectures and datasets demonstrate that the method outperforms prior differential privacy-based techniques in both retrieval and generation performance while offering comparable empirical privacy.
By Dixi Yao, Tahseen Rabbani, Tian Li
arXiv:2609.38934v1 Announce Type: cross
Abstract: Differentially private (DP) text generation can protect individual records, but privacy alone does not specify what evidence a released statement car...
By Tsubasa Takahashi, Takumi Hiraoka
arXiv:2601. 06471v2 Announce Type: replace-cross Abstract: Large language model (LLM) personalization aims to adapt general-purpose models to individual users.
By Junho Park, Dohoon Kim, Taesup Moon
arXiv:2603. 19258v2 Announce Type: replace-cross Abstract: Differentially private (DP) fine-tuning of large language models (LLMs) requires massive compute and full model access, which rules out state-of-the-art proprietary APIs for general users.
By Eli Chien, Yuzheng Hu, Ryan McKenna, Shanshan Wu, Zheng Xu, Peter Kairouz
arXiv:2606. 18782v1 Announce Type: cross Abstract: Large Language Models are increasingly applied to sensitive domains that require redaction of personally identifiable information (PII).
By Sean Brynj\'olfsson, Shashvat Jayakrishnan, Esha Sali, Diptanshu Purwar, Madhav Aggarwal
The paper proposes a new approach to watermarking language models that balances privacy and provenance. By shifting the focus from tracking individual texts to estimating aggregate usage, the authors develop locally differentially private watermarks that limit the evidence any single text can contribute to detection. The Red‑Green Sequence (RGS) watermark achieves accurate frequency estimation with low error rates even at stringent privacy levels (ε=1–2) across various models and realistic deployment scenarios.
By Elise L. Han, Tatsunori Hashimoto
arXiv:2606. 01849v1 Announce Type: new Abstract: Differentially private (DP) text synthesis promises to unlock sensitive corpora for model training, but it remains unclear whether DP synthetic data transmits genuinely new knowledge and capabilities present only in those corpora.
By Peihan Liu, Lucas Rosenblatt, Weiwei Kong, Natalia Ponomareva, Gautam Kamath, Rachel Cummings, Roxana Geambasu, Yu Gan, Lillian Tsai, Alex Bie