zkFinGPT: Zero-Knowledge Proofs for Financial Generative Pre-trained Transformers
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912840665792512 |
|---|---|
| author | Liu, Xiao-Yang Li, Ningjie Wang, Keyi Zhi, Xiaoli Tong, Weiqin |
| author_facet | Liu, Xiao-Yang Li, Ningjie Wang, Keyi Zhi, Xiaoli Tong, Weiqin |
| contents | Financial Generative Pre-trained Transformers (FinGPT) with multimodal capabilities are now being increasingly adopted in various financial applications. However, due to the intellectual property of model weights and the copyright of training corpus and benchmarking questions, verifying the legitimacy of GPT's model weights and the credibility of model outputs is a pressing challenge. In this paper, we introduce a novel zkFinGPT scheme that applies zero-knowledge proofs (ZKPs) to high-value financial use cases, enabling verification while protecting data privacy. We describe how zkFinGPT will be applied to three financial use cases. Our experiments on two existing packages reveal that zkFinGPT introduces substantial computational overhead that hinders its real-world adoption. E.g., for LLama3-8B model, it generates a commitment file of $7.97$MB using $531$ seconds, and takes $620$ seconds to prove and $2.36$ seconds to verify. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15716 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | zkFinGPT: Zero-Knowledge Proofs for Financial Generative Pre-trained Transformers Liu, Xiao-Yang Li, Ningjie Wang, Keyi Zhi, Xiaoli Tong, Weiqin Computational Engineering, Finance, and Science Cryptography and Security Financial Generative Pre-trained Transformers (FinGPT) with multimodal capabilities are now being increasingly adopted in various financial applications. However, due to the intellectual property of model weights and the copyright of training corpus and benchmarking questions, verifying the legitimacy of GPT's model weights and the credibility of model outputs is a pressing challenge. In this paper, we introduce a novel zkFinGPT scheme that applies zero-knowledge proofs (ZKPs) to high-value financial use cases, enabling verification while protecting data privacy. We describe how zkFinGPT will be applied to three financial use cases. Our experiments on two existing packages reveal that zkFinGPT introduces substantial computational overhead that hinders its real-world adoption. E.g., for LLama3-8B model, it generates a commitment file of $7.97$MB using $531$ seconds, and takes $620$ seconds to prove and $2.36$ seconds to verify. |
| title | zkFinGPT: Zero-Knowledge Proofs for Financial Generative Pre-trained Transformers |
| topic | Computational Engineering, Finance, and Science Cryptography and Security |
| url | https://arxiv.org/abs/2601.15716 |