撤回文章:通过区块链保护医疗保健数据,使协作机器学习成为可能
C U Om Kumar1, Sudhakaran Gajendran2, V Balaji3
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, India.
Soft computing
|June 8, 2023
概括
本研究介绍了一种使用联合学习和区块链的保护隐私的模型传输方法. 它成功地共享机器学习模型,并以代币奖励贡献者,增强安全的数据协作.
科学领域:
- 机器学习 机器学习
- 区块链技术 区块链技术
- 数据 隐私 数据 隐私 数据
背景情况:
- 机器学习中的传统数据传输引发了隐私问题,特别是在医疗保健领域.
- 集中式数据传输方法是有限的,并带来安全风险.
- 需要分散的方法来实现安全和高效的模型交换.
研究的目的:
- 调查使用联合学习的用户和组织之间的隐私保护模型转移.
- 探索区块链技术的使用,以奖励客户贡献.
- 为机器学习模型共享建立安全高效的分散框架.
主要方法:
- 联邦学习技术用于维护隐私的模型培训和转移.
- 区块链技术用于激励和奖励参与客户使用代币.
- 利用COVID-19数据集来评估联合学习过程.
主要成果:
- 联合学习方法使得用户和志愿者组织之间成功的模式转移成为可能.
- 客户贡献者通过区块链有效地获得了代币的奖励.
- 个别模型的准确率达到88% (贡献者a),85% (贡献者b) 和74% (贡献者c).
- 该 FedAvg 算法实现了 82% 的整体准确性.
结论:
- 联合学习为安全,分散的模型转移提供了可行的解决方案.
- 区块链集成提供了一个强大的机制,以奖励在联合学习系统中的贡献.
- 拟议的框架加强了机器学习应用中的协作和数据隐私.
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