走向为公平和保护隐私的图形神经网络提供统一框架
Xuemin Wang1, Yunhui Li2, Tianlong Gu3
1School of Information and Communications, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China; Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China.
概括
本研究介绍了FPGNN,它是图形神经网络 (GNN) 的统一框架,可以平衡公平和隐私. 它使用基于排名的公平性和对抗性培训来减轻高风险应用中的风险.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 在关键应用中,图形神经网络 (GNN) 引发了公平性和隐私方面的担忧.
- 现有的方法独立地解决公平性和隐私问题,造成冲突.
- 基于距离的公平方法可以无意中增加隐私风险,如属性推断.
研究的目的:
- 为公平和保护隐私的GNN提出一个统一的框架,FPGNN.
- 在本地差异隐私 (LDP) 下引入PL-FPGNN来处理噪音敏感属性.
- 从理论和经验上证明拟议方法的有效性.
主要方法:
- 开发了FPGNN,使用可差异化排名方法来确定个人公平性.
- 在FPGNN内部实施对抗训练,以保护嵌入式中的敏感信息.
- 在PL-FPGNN中引入了前置校正,以解决LDP保护属性的噪声问题.
主要成果:
- FPGNN实现了基于排名的个人公平,避免了隐私泄露的放大.
- PL-FPGNN可稳定地处理噪音敏感属性.
- 实验表明FPGNN和PL-FPGNN平衡公平性,隐私和预测准确性.
结论:
- 拟议的FPGNN框架为GNN中的公平性和隐私提供了统一的解决方案.
- PL-FPGNN为具有噪音敏感属性的场景提供了强大的方法.
- 基于排名的公平性方法在理论上是合理的,在实践中是有效的.
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