دورية أكاديمية
IHVFL: a privacy-enhanced intention-hiding vertical federated learning framework for medical data
| العنوان: | IHVFL: a privacy-enhanced intention-hiding vertical federated learning framework for medical data |
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| المؤلفون: | Fei Tang, Shikai Liang, Guowei Ling, Jinyong Shan |
| المصدر: | Cybersecurity, Vol 6, Iss 1, Pp 1-17 (2023) |
| بيانات النشر: | SpringerOpen, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Computer engineering. Computer hardware LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | Medical data, Vertical federated learning, Privacy-presserving, Intention-hiding, Logistic regression, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | Abstract Vertical Federated Learning (VFL) has many applications in the field of smart healthcare with excellent performance. However, current VFL systems usually primarily focus on the privacy protection during model training, while the preparation of training data receives little attention. In real-world applications, like smart healthcare, the process of the training data preparation may involve some participant’s intention which could be privacy information for this participant. To protect the privacy of the model training intention, we describe the idea of Intention-Hiding Vertical Federated Learning (IHVFL) and illustrate a framework to achieve this privacy-preserving goal. First, we construct two secure screening protocols to enhance the privacy protection in feature engineering. Second, we implement the work of sample alignment bases on a novel private set intersection protocol. Finally, we use the logistic regression algorithm to demonstrate the process of IHVFL. Experiments show that our model can perform better efficiency (less than 5min) and accuracy (97%) on Breast Cancer medical dataset while maintaining the intention-hiding goal. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2523-3246 |
| Relation: | https://doaj.org/toc/2523-3246 |
| DOI: | 10.1186/s42400-023-00166-9 |
| URL الوصول: | https://doaj.org/article/798320fec85e4981a5879c8f760c7c89 |
| رقم الأكسشن: | edsdoj.798320fec85e4981a5879c8f760c7c89 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 25233246 |
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| DOI: | 10.1186/s42400-023-00166-9 |