副图层级的联合图形神经网络,用于保护隐私的建议与元学习
Zhaoxing Han1, Chengyu Hu2, Tongyaqi Li1
1School of Cyber Science and Technology, Shandong University, Qingdao, 266237, Shandong, China.
概括
本研究引入了一个为保护隐私的图形神经网络 (GNN) 建议的联合框架. 它通过使用软件守护扩展 (SGX) 和本地差异隐私 (LDP) 来增强数据隐私和模型准确性.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 数据 隐私 数据 隐私 数据
背景情况:
- 推系统中的传统图形神经网络 (GNN) 面临着由于数据处理的集中而导致的隐私挑战.
- 联合学习提供了去中心化的方法,但需要强大的隐私保护机制.
研究的目的:
- 为基于GNN的保护隐私的建议制定一个联合框架.
- 在分布式推系统中增强数据隐私和模型性能.
主要方法:
- 使用本地用户数据实现了分布式GNN培训的联合框架.
- 使用软件守护扩展 (SGX) 进行安全的子图交换和扩展.
- 应用局部差分隐私 (LDP) 以确保梯度聚合期间的数据隐私.
- 嵌入式原型网络 (PN) 和模型不可知的元学习 (MAML) 进行个性化的建议和处理数据异质性.
主要成果:
- 拟议的联合框架显著优于基于GNN的集中推方法.
- 该系统有效地保护用户隐私,同时保持高推准确度.
- 在六个不同的数据集中展示了优越性.
结论:
- 联邦GNN框架为保护隐私的建议提供了一个可行的解决方案.
- 在联合学习环境中,SGX和LDP有效地减轻隐私风险.
- 个性化技术提高了对异质客户数据的适应性.
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