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一个以服务为导向的微服务框架,用于工业物联网智能应用中的基于隐私的差异性保护
Dileep Kumar Murala1, K Vara Prasada Rao1, Veera Ankalu Vuyyuru2
1Department of Computer Science and Engineering, Faculty of Science and Technology, ICFAI Foundation for Higher Education, Hyderabad, Telangana, 50120, India.
Scientific reports
|August 9, 2025
概括
本研究介绍了工业物联网的保护隐私的微服务架构,集成差异隐私来保护敏感数据,同时保持高准确度和减少延迟,以确保安全,智能工业系统.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 工业物联网的工业物联网.
背景情况:
- 人工智能,物联网和边缘云计算的进步推动了智能行业的转型.
- 边缘和云计算为智能分析提供可扩展的资源.
- 数据隐私和安全是分布式环境中人工智能采用的关键挑战.
研究的目的:
- 为智能工业物联网 (IIoT) 应用提出一个保护隐私,面向服务的微服务架构.
- 将差异隐私 (DP) 机制集成到机器学习管道中,以加强数据保护.
- 支持集中和分布式部署,以实现灵活,可扩展和安全的分析.
主要方法:
- 开发了一个微服务架构,集成了差异隐私 (DP) 机制.
- 实施和评估差异化的私有辐射基函数网络 (RBFNs).
- 在各种隐私预算 (ɛ) 中使用现实世界和合成物联网数据集的测试模型.
主要成果:
- 拟议的框架保持了高预测准确性 (高达96.72%) 与可接受的隐私保证.
- 基于微服务的部署实现了与单一基线相比,平均延迟减少了28.4%.
- 不同的隐私机制有效地保护了敏感的培训数据.
结论:
- 拟议的架构是有效和实用的,可以为IIoT提供保护隐私,高效和可扩展的智能.
- 微服务设计提高了计算效率,并通过动态服务编排减少了延迟.
- 证明了在IIoT中部署强大的,关注隐私的AI服务,以确保安全的工业系统的可行性.
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