anonymization

A concept for a privacy middleware that anonymizes sensitive data before sending to LLMs to protect privacy, with local placeholder mapping, seeking feedback on feasibility.

This study audits demographic leakage in German LLM-generated resumes, finding that despite anonymization, classifiers can reliably distinguish between male and female names due to subtle linguistic differences, raising fairness concerns in AI hiring pipelines.

This paper investigates the privacy-personalization trade-off in LLMs by reducing stylistic signals in user-specific text generation, finding that anonymization lowers stylistic fidelity while preserving semantic meaning.

A study shows that anonymized patient records from decades past in AI training datasets can cause models to misdiagnose current patients by recalling historical health states, increasing the risk of errors in diagnoses.

This paper presents a systematic empirical study on the trade-off between privacy and performance in large language models when anonymizing input data, finding that anonymization degrades performance with effects varying by model capability and task type.

Introduces GRASP, a method that uses Group Relative Policy Optimization to train a small on-device language model for adversarial anonymization, improving the privacy-utility trade-off over DPO-distilled baselines while running at a fraction of the cost of frontier teacher models.

TIM PG is a strictly offline Windows utility that automatically masks personal data from the clipboard before pasting into AI tools, ensuring secure local data privacy without cloud reliance.

Introduces Knowledgeless Language Models (KLLMs), pretrained on corpora with anonymized entities to suppress parametric recall and enhance evidence-grounded reasoning, achieving substantial improvements on contextual QA, fact verification, and hallucination detection benchmarks.

This paper investigates whether LLMs can identify their own model family from stylometric fingerprints in role-constrained political analysis texts, even after prompt-level anonymization. The findings confirm that anonymization is insufficient and have implications for EU AI Act compliance and multi-agent system validation.

AURA is an LLM-powered anonymization framework that balances privacy protection against agentic web-search re-identification while preserving contextual utility through adaptive privacy scopes and mask-reconstruct methods.

This case study empirically investigates where anonymization should be applied in Retrieval-Augmented Generation (RAG) pipelines to balance privacy and utility, examining the impact of anonymization at different stages (dataset vs. generated answer) to inform privacy risk mitigation strategies.