Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929449048473600 |
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| author | Khan, Md. Saikat Islam Gupta, Aparna Seneviratne, Oshani Patterson, Stacy |
| author_facet | Khan, Md. Saikat Islam Gupta, Aparna Seneviratne, Oshani Patterson, Stacy |
| contents | We introduce Federated Learning for Relational Data (Fed-RD), a novel privacy-preserving federated learning algorithm specifically developed for financial transaction datasets partitioned vertically and horizontally across parties. Fed-RD strategically employs differential privacy and secure multiparty computation to guarantee the privacy of training data. We provide theoretical analysis of the end-to-end privacy of the training algorithm and present experimental results on realistic synthetic datasets. Our results demonstrate that Fed-RD achieves high model accuracy with minimal degradation as privacy increases, while consistently surpassing benchmark results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01609 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection Khan, Md. Saikat Islam Gupta, Aparna Seneviratne, Oshani Patterson, Stacy Computational Engineering, Finance, and Science We introduce Federated Learning for Relational Data (Fed-RD), a novel privacy-preserving federated learning algorithm specifically developed for financial transaction datasets partitioned vertically and horizontally across parties. Fed-RD strategically employs differential privacy and secure multiparty computation to guarantee the privacy of training data. We provide theoretical analysis of the end-to-end privacy of the training algorithm and present experimental results on realistic synthetic datasets. Our results demonstrate that Fed-RD achieves high model accuracy with minimal degradation as privacy increases, while consistently surpassing benchmark results. |
| title | Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2408.01609 |