arXiv AI

AWED-PIPER: Agents, Web Applications & Expert Detectors for Personally Identifiable Information Protection & Fine-grained Named Entity Recognition across 36 languages for 6.6 Billion Speakers

arXiv:2601. 10161v3 Announce Type: replace-cross Abstract: Named Entity Recognition (NER) and Personally Identifiable Information (PII) anonymization are critical tasks in Natural Language Processing (NLP) for information extraction and privacy preservation.

arXiv AI

Aug 7

OpenAI Privacy Filter: A Cross-Lingual, Cross-Domain PII Evaluation Across 32 Benchmarks

arXiv:2608. 02616v2 Announce Type: replace-cross Abstract: We present what is, to our knowledge, the first systematic evaluation of OpenAI's Privacy Filter (OPF), a 1.

By Rohith Uppala

llms fine-tuning benchmarks safety

arXiv AI

Aug 5

Evaluating OpenAI's Privacy Filter: Cross-Lingual, Cross-Domain PII Detection Across 42 Benchmarks

arXiv:2608. 02616v1 Announce Type: cross Abstract: We present the first independent, systematic evaluation of OpenAI's Privacy Filter (OPF), a 1.

By Rohith Uppala

llms benchmarks safety

arXiv AI

Jun 24

Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity

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

llms rag agents safety

arXiv AI

Jun 18

RedactionBench

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

llms agents nlp benchmarks safety

arXiv Machine Learning

Aug 7

Where Privacy Risk Lives in English-Source Multilingual RAG: A Stage-Decomposed Audit Across Five Query Languages

arXiv:2608. 05163v1 Announce Type: cross Abstract: A common assumption holds that switching to a non-English language makes a multilingual RAG system easier to attack for personal information.

By Yanhang Li, Zhichao Fan, Zexin Zhuang

llms rag benchmarks safety

arXiv AI

5d ago

Privacy-Preserving RAG by Concealing Sensitive Information from External LLMs

arXiv:2608. 12675v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely used to improve the performance of Large Language Models (LLMs) in answering user queries.

By Saleh Almohaimeed, Saad Almohaimeed, Mousa Jari, Fahad Alotaibi, Khalid A. Alobaid

llms rag fine-tuning safety

arXiv Machine Learning

Jun 24

Natural Identifiers for Privacy and Data Audits in Large Language Models

arXiv:2606. 24408v1 Announce Type: new Abstract: Assessing the privacy of large language models (LLMs) presents significant challenges.

By Lorenzo Rossi, Bart{\l}omiej Marek, Franziska Boenisch, Adam Dziedzic

llms safety

arXiv AI

Jul 28

PANOPTICON: A PII-Based Assemblage of Naturalistic Output Tokens for Investigating Privacy Leakage Within LLM Context Window

arXiv:2607. 22695v1 Announce Type: new Abstract: Large Language Models (LLMs) are capable of generalizing human language for the completion of never-before-seen tasks, leading to widespread deployment.

By Ryan Thornton, Mir Mehedi Ahsan Pritom, Maanak Gupta

llms benchmarks safety

arXiv AI

Jun 2

GuidaPA: Privacy-Preserving Chatbot for Public Administration via Federated Learning

arXiv:2606. 01386v1 Announce Type: new Abstract: We present GuidaPA, a privacy-preserving chatbot for the Italian Public Administration (PA) trained via Federated Learning (FL) on documentation from two national PA platforms, SIGESON and SIDFORS.

By Daniel M. Jimenez-Gutierrez, Albenzio Cirillo, Raffaele Nicolussi, Alessio Beltrame, Andrea Vitaletti

llms fine-tuning safety

arXiv AI

Jun 18

Redact or Keep? A Fully Local AI Cascade for Educational Dialogue De-Identification

arXiv:2606. 18372v1 Announce Type: cross Abstract: Educational dialogue is a valuable but sensitive resource for research: the same transcripts that capture authentic learning often capture personally identifiable information (PII) entangled with curricular content, where "Riemann" may refer to a real student or to a mathematical concept.

By Haocheng Zhang, Zhuqian Zhou, Kirk Vanacore, Bakhtawar Ahtisham, Ren\'e F. Kizilcec

llms nlp safety

arXiv AI

Jun 10

IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts

arXiv:2606. 09908v1 Announce Type: cross Abstract: Large language models (LLMs) are becoming widely deployed as personal AI assistants with access to sensitive user data, making privacy a major challenge for their design and evaluation.

By Ayana Hussain, Soumya Sharma, Golnoosh Farnadi, Nicholas Vincent, H\'eber Hwang Arcolezi, Ulrich A\"ivodji

llms benchmarks safety

arXiv AI

Jun 16

CHILLGuard: Towards Fine-Grained Chinese LLM Safety Guardrail with Scalable Data Construction and Model-aware Preference Alignment

arXiv:2606. 15396v1 Announce Type: cross Abstract: Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns.

By Wenbo Yu, Bohua Wang, Hao Fang, Kuofeng Gao, Jingru Zeng, Xiaochen Yang, Tianyi Zhang, Xiaoxiao Ma, Jiawei Kong, Hao Wu, Bin Chen, Shu-Tao Xia, Min Zhang

llms rag benchmarks safety

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