On the accuracy and efficiency of group-wise clipping in differentially private optimization

[PDF] arxiv.org

Preserving privacy in large language models: A survey on current threats and solutions

M Miranda, ES Ruzzetti, A Santilli, FM Zanzotto… - arXiv preprint arXiv …, 2024 - arxiv.org
Large Language Models (LLMs) represent a significant advancement in artificial
intelligence, finding applications across various domains. However, their reliance on …
被引用次数：5 相关文章 所有 3 个版本
[PDF] arxiv.org

Llm-pbe: Assessing data privacy in large language models

Q Li, J Hong, C Xie, J Tan, R Xin, J Hou, X Yin… - arXiv preprint arXiv …, 2024 - arxiv.org
Large Language Models (LLMs) have become integral to numerous domains, significantly
advancing applications in data management, mining, and analysis. Their profound …
被引用次数：12 相关文章 所有 7 个版本
[PDF] arxiv.org

Pre-training Differentially Private Models with Limited Public Data

Z Bu, X Zhang, M Hong, S Zha, G Karypis - arXiv preprint arXiv …, 2024 - arxiv.org
The superior performance of large foundation models relies on the use of massive amounts
of high-quality data, which often contain sensitive, private and copyrighted material that …
被引用次数：2 相关文章 所有 2 个版本
[PDF] arxiv.org

Can large language models be privacy preserving and fair medical coders?

A Dadsetan, D Soleymani, X Zeng… - arXiv preprint arXiv …, 2024 - arxiv.org
Protecting patient data privacy is a critical concern when deploying machine learning
algorithms in healthcare. Differential privacy (DP) is a common method for preserving …
相关文章 所有 2 个版本