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保护隐私的合成数据生成方法用于物联网传感器网络IDS使用CTGAN.

Saleh Alabdulwahab1, Young-Tak Kim2,3, Yunsik Son4

  • 1Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Republic of Korea.

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概括

本研究介绍了一种保护隐私的方法,用于使用条件表式生成对抗网络 (CTGAN) 和差异隐私 (DP) 来生成合成物联网数据. 这种方法提高了入侵检测系统 (IDS) 的数据实用性,同时最大限度地降低了隐私风险.

关键词:
物联网的物联网,就是物联网.数据实用程序数据实用程序深度学习是一种深度学习.不同的隐私差异 隐私差异生成性的对抗性网络.侵入检测系统的入侵检测系统

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科学领域:

  • 网络安全 网络安全
  • 数据 隐私 数据 隐私 数据
  • 机器学习 机器学习

背景情况:

  • 物联网 (IoT) 网络面临着越来越多的隐私风险,原因是机器学习 (ML) 用于入侵检测系统 (IDS) 的大型数据集.
  • 在数据存储和机器学习模型培训方面依赖第三方加剧了这些隐私问题.
  • 现有的方法努力平衡IDS的数据实用性与强大的隐私保护.

研究的目的:

  • 为物联网传感器网络数据提出一种新的保护隐私的合成数据生成方法.
  • 保持基于ML的IDS数据的实用性,同时保护敏感信息.
  • 为了减轻与数据共享和第三方处理相关的隐私风险.

主要方法:

  • 使用条件表式生成对抗网络 (CTGAN) 进行合成数据生成.
  • 通过受控的噪音注入,与CTGAN集成差异隐私 (DP).
  • 采用动态分布调整和量子匹配来优化公用事业-隐私权的权衡.

主要成果:

  • 与标准DP方法相比,在数据实用性方面取得了显著的改进,KS测试得分为0.80.
  • 有效地将隐私风险降到最低,包括挑选,可链接和推断攻击.
  • 展示了合成数据集的能力,以支持有效的入侵检测.

结论:

  • 拟议的DP增强型CTGAN方法为物联网环境中保护隐私的合成数据生成提供了强大的解决方案.
  • 这种方法成功地平衡了IDS的数据实用性与强有力的隐私保证.
  • 能够安全地利用物联网数据来开发有效的入侵检测系统,而不影响用户隐私.