加强金融交易的安全性:一种基于区块链的新型联合学习框架,用于检测金融科技中的假冒数据
Hasnain Rabbani1, Muhammad Farrukh Shahid1, Tariq Jamil Saifullah Khanzada2,3
1Computer Science, FAST School of Computing, FAST-NUCES, Karachi, Sindh, Pakistan.
PeerJ. Computer science
|December 9, 2024
概括
联合学习和区块链集成通过保护消费者数据来增强金融安全. 该框架使用机器学习模型来防止欺诈行为,而不是在机构之间共享敏感客户信息.
科学领域:
- 金融技术 (金融科技) 的发展
- 网络安全 网络安全
- 数据 隐私 数据 隐私 数据
背景情况:
- 金融科技利用技术提供高效的金融服务,但数据泄露带来重大风险.
- 传统的机器学习 (ML) 模型的数据共享损害了客户的隐私.
- 保护敏感的金融数据对于保持客户信任和监管合规至关重要.
研究的目的:
- 提出一个新的框架,将联合学习 (FL) 和区块链结合起来,以提高财务安全.
- 保护消费者免受欺诈交易,同时保护数据隐私.
- 为了使协作式的ML模型培训能够在没有机构间数据交换的情况下进行.
主要方法:
- 实施一个使用多个机器学习 (ML) 模型的联合学习 (FL) 框架.
- 整合区块链技术,为FL提供安全和透明的平台.
- 在各个机构的客户数据上进行本地模式培训,然后在中央服务器上进行聚合.
主要成果:
- 拟议的框架有效地保护消费者免受欺诈交易的影响.
- 客户隐私受到保护,因为机构之间没有交换或存储私人客户数据.
- 区块链确保数据交换和模型培训流程的不变和可审计的记录.
结论:
- 将联合学习 (FL) 与区块链相结合,为金融科技中的安全和私有数据协作提供了强大的解决方案.
- 这种方法显著减轻了与金融部门数据泄露相关的风险.
- 该框架有助于开发先进的欺诈检测模型,同时保持严格的数据隐私标准.
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