arXiv AI By Ruiyi Yang, Gayathri Lihinikaduarachchi, Rahat Masood, Flora D. Salim, Salil S. Kanhere

GuardianAgent: Policy-Conditioned Risk-Adaptive Anonymization with Verified Adversarial Escalation

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arXiv Computation and Language
Oct 27

"I Strongly Suspect This Website Is a Scam": Benchmarking PII Leakage and Detection without Defense in Autonomous Web Agents

arXiv:2606.00497v2 Announce Type: replace-cross Abstract: Deceptive web content, widely instantiated across the internet and commonly known as \textit{social-engineering attacks}, manipulates autonom...

By Soham Roy, Sarthakbrata Halder, Arya Bharaty, Vaibhav Bhaskar, Yash Sinha, Dhruv Kumar, Srikant Panda, Murari Mandal
arXiv Computation and Language
Oct 5

LLM Anonymization Against Agentic Re-Identification

The paper introduces AURA, an LLM-powered mask‑reconstruct framework for anonymizing text while preserving utility. It decouples privacy localization from utility‑preserving reconstruction and uses adversarial checks to select candidates. Experiments on real‑user interview transcripts show that AURA achieves the lowest agentic re‑identification rates among non‑DP methods and retains more contextual utility than prior LLM anonymizers.

By Ziwen Li, Jianing Wen, Tianshi Li
arXiv Computation and Language
Sep 11

On the Impact of Anonymization on the Performance of Large Language Models

The paper systematically studies how anonymizing input data affects large language models (LLMs). Five prominent LLMs were evaluated on eleven benchmarks, comparing performance on original versus pseudonymized inputs. Results show that anonymization generally degrades performance, with larger drops for more capable models and task-dependent effects; reversible anonymization preserves entity uniqueness better than irreversible redaction, and prompting about anonymization offers no benefit.

By Tobias Deu{\ss}er, Max Hahnb\"uck, Lorenz Sparrenberg, Tobias Uelwer, Christian Bauckhage, Rafet Sifa
arXiv Computation and Language
Oct 19

Veil-Then-Reveal: Protecting PII Privacy via Reversible Pseudo-Anonymization in Zero-Trust User--LLM Interaction

The paper introduces Veil-then-Reveal (VtR), a user-side framework that pseudo‑anonymizes personally identifiable information (PII) in prompts sent to large language models (LLMs) under a zero‑trust model. VtR replaces detected PII with realistic, same‑type surrogates using format‑preserving encryption for structured data and a keyed‑hash dictionary for semantic data, while preserving linguistic and structural cues. The system achieves high detection recall (97.9%), strong protection of detected values (95.2%), and near‑perfect round‑trip restoration (99.9%) across three datasets.

By Mohamed Shaaban, Mohamed Elmahallawy, Thomas E. Carroll