基于改进的自适应联合学习和WGAN的银行数据保护和欺诈识别
1School of Economics Management and Law, Jilin Normal University, Siping, 136000, China. zyj1414867121@126.com.
Scientific reports
|July 2, 2025
概括
本研究介绍了一种适应性数据保护架构,使用联合学习和差异隐私来保护银行数据并检测欺诈行为. 该系统实现了高精度 (0.996),隐私泄露最小,增强了财务安全和客户信任.
科学领域:
- 计算机科学 计算机科学
- 信息安全 信息安全
- 数据 隐私 数据 隐私 数据
背景情况:
- 银行面临越来越多的数据隐私和交易欺诈威胁.
- 现有系统在平衡安全性,效率和隐私保护方面扎.
研究的目的:
- 为银行开发一个安全有效的数据隐私保护系统.
- 通过使用先进的隐私保护技术,有效地识别交易欺诈.
- 加强银行数据隐私能力,降低金融风险.
主要方法:
- 提出了改进的自适应数据保护架构,将联合学习和差异隐私相结合.
- 设计了一个利用生成对抗网络的欺诈识别模型.
- 实施了适应性梯度修剪和噪声分化策略.
主要成果:
- 适应性数据保护架构改善了损失函数收 (最小损失为0.09) 和学习能力.
- 在低隐私预算下实现高精度 (0.996) 具有显著的优势.
- 即使在攻击后,隐私泄露概率很低,相互信息最小 (0.02) 和重新识别风险 (0.194).
- 欺诈识别模型显示出卓越的功能和精度,错误和错误报警率在0.15-0.35.5之间.
结论:
- 这项研究成功地提高了银行数据隐私保护能力.
- 拟议的系统有效地识别交易欺诈,同时保持数据隐私.
- 实施有助于降低金融风险,增强客户对银行机构的信任.
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