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瓦斯斯坦生成对抗性网络基于差异性隐私 逆向数据共享
IEEE journal of biomedical and health informatics
|June 16, 2023
概括
本研究介绍了Wasserstein生成对抗网络 (WGAN) 的差异性隐私元向数据共享. 拟议的WGAN模型和算法有效地平衡了元宇宙环境中的数据实用性和隐私.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 差异性隐私对于保护元宇宙共享中的敏感数据至关重要.
- 随机数据干扰可以在数据实用性和隐私之间进行权衡.
研究的目的:
- 提出和分析使用瓦斯斯坦生成对抗网络 (WGAN) 进行差异隐私元宇宙数据共享的模型和算法.
- 为了解决数据实用性和隐私之间的不平衡,在metaverse数据共享.
主要方法:
- 通过将规范化术语集成到WGAN中,构建了差异性隐私元宇宙数据共享的数学模型.
- 开发并理论分析了基于WGAN的差异隐私元宇宙数据共享的基本和联合模型和算法.
- 雇员为联邦模式提供序列化培训.
主要成果:
- 实验结果验证了对拟议算法的理论分析.
- 基于WGAN的算法证明了数据隐私和实用性之间的有效平衡.
- 基于实用性和隐私指标的比较分析证实了这些发现.
结论:
- 瓦斯斯坦的生成对抗性网络为安全和有用的差异隐私metaverse数据共享提供了可行的解决方案.
- 拟议的模型和算法成功地保持了隐私和实用性之间的平衡在元宇宙数据共享.
- 这项研究有助于在虚拟环境中推进隐私保护技术.
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