When FinTech Meets Privacy: Securing Financial LLMs with Differential Private Fine-Tuning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911149199458304 |
|---|---|
| author | Zhu, Sichen Leung, Hoyeung Wang, Xiaoyi Wei, Jia Xu, Honghui |
| author_facet | Zhu, Sichen Leung, Hoyeung Wang, Xiaoyi Wei, Jia Xu, Honghui |
| contents | The integration of Large Language Models (LLMs) into financial technology (FinTech) has revolutionized the analysis and processing of complex financial data, driving advancements in real-time decision-making and analytics. With the growing trend of deploying AI models on edge devices for financial applications, ensuring the privacy of sensitive financial data has become a significant challenge. To address this, we propose DPFinLLM, a privacy-enhanced, lightweight LLM specifically designed for on-device financial applications. DPFinLLM combines a robust differential privacy mechanism with a streamlined architecture inspired by state-of-the-art models, enabling secure and efficient processing of financial data. This proposed DPFinLLM can not only safeguard user data from privacy breaches but also ensure high performance across diverse financial tasks. Extensive experiments on multiple financial sentiment datasets validate the effectiveness of DPFinLLM, demonstrating its ability to achieve performance comparable to fully fine-tuned models, even under strict privacy constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08995 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When FinTech Meets Privacy: Securing Financial LLMs with Differential Private Fine-Tuning Zhu, Sichen Leung, Hoyeung Wang, Xiaoyi Wei, Jia Xu, Honghui Cryptography and Security The integration of Large Language Models (LLMs) into financial technology (FinTech) has revolutionized the analysis and processing of complex financial data, driving advancements in real-time decision-making and analytics. With the growing trend of deploying AI models on edge devices for financial applications, ensuring the privacy of sensitive financial data has become a significant challenge. To address this, we propose DPFinLLM, a privacy-enhanced, lightweight LLM specifically designed for on-device financial applications. DPFinLLM combines a robust differential privacy mechanism with a streamlined architecture inspired by state-of-the-art models, enabling secure and efficient processing of financial data. This proposed DPFinLLM can not only safeguard user data from privacy breaches but also ensure high performance across diverse financial tasks. Extensive experiments on multiple financial sentiment datasets validate the effectiveness of DPFinLLM, demonstrating its ability to achieve performance comparable to fully fine-tuned models, even under strict privacy constraints. |
| title | When FinTech Meets Privacy: Securing Financial LLMs with Differential Private Fine-Tuning |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2509.08995 |