用对比学习降低工业物联网数据泄露风险
Contrastive Learning for Privacy Enhancements in Industrial Internet of Things
- 通过自监督对比学习减少对敏感数据和标签的依赖
- 针对工业场景特性提出隐私保护新方法
- 适合关注工业数据安全的研究者与工程师
工业互联网（IIoT）将智能感知、通信与分析融入制造、能源等工业环境，虽支持预测性维护与跨站点优化，但也因操作数据敏感性带来显著隐私与保密风险。对比学习作为一种自监督表征学习范式，因其减少对标注数据和原始数据共享的依赖，正成为隐私保护分析的有前景方案。尽管对比学习在物联网领域已有探索，本文首次系统综述其在工业互联网中的隐私保护应用，强调工业数据特性、系统架构及多样化应用场景的独特性，讨论现有解决方案与开放挑战，并展望未来研究方向。
原文摘要 · Abstract (English)
The Industrial Internet of Things (IIoT) integrates intelligent sensing, communication, and analytics into industrial environments, including manufacturing, energy, and critical infrastructure. While IIoT enables predictive maintenance and cross-site optimization of modern industrial control systems, such as those in manufacturing and energy, it also introduces significant privacy and confidentiality risks due to the sensitivity of operational data. Contrastive learning, a self-supervised representation learning paradigm, has recently emerged as a promising approach for privacy-preserving analytics by reducing reliance on labeled data and raw data sharing. Although contrastive learning-based privacy-preserving techniques have been explored in the Internet of Things (IoT) domain, this paper offers a comprehensive review of these techniques specifically for privacy preservation in Industrial Internet of Things (IIoT) systems. It emphasizes the unique characteristics of industrial data, system architectures, and various application scenarios. Additionally, the paper discusses solutions and open challenges and outlines future research directions.
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