教师数据隐私保护的联合学习:在PIPL的背景下的一项研究
Shanwei Chen1, Xiu Zhi Qi2, Xue Hui Han1
1College of Education, Baoji University of Arts and Sciences, Baoji, China.
Frontiers in big data
|February 25, 2026
概括
联合学习 (FL) 加强了中国教师数据隐私.
科学领域:
- 计算机科学 计算机科学
- 法律 法律 法律 法律
- 教育教育教育教育教育教育.
背景情况:
- 中国的个人信息保护法 (PIPL) 规定了严格的数据隐私,特别是对于教育工作者.
- 集中式机器学习可能会导致数据泄露和教育环境中的违规行为.
- 联合学习 (FL) 提供了一种去中心化的方法,用于维护隐私的协作模式培训.
研究的目的:
- 评估联邦学习 (FL) 框架是否符合中国个人信息保护法 (PIPL).
- 评估FL内部增强隐私技术对保护教师数据的有效性.
- 建议在教育中为保护隐私的机器学习提出一个合规框架.
主要方法:
- 使用定量模拟和定性合规分析来评估FL框架.
- 不同隐私被经验验证为隐私保障的关键机制.
- 安全多方计算 (SMC) 在理论上对PIPL对齐进行了分析.
主要成果:
- 与集中方法相比,FL模拟显示数据泄露风险降低.
- 飞机通过本地处理,差异隐私和安全聚合实现了PIPL合规.
- 在保持模型性能的同时,证明了更好的隐私保护.
结论:
- 联邦学习 (FL) 在概念上支持PIPL下的教师数据隐私.
- 提出了一个定制的合规框架,整合FL和增强隐私的技术.
- 为教育机构和实施隐私保护人工智能的机构提供了建议.
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