对比学习在工业互联网中的隐私增强应用
Contrastive Learning for Privacy Enhancements in Industrial Internet of Things
AI总结：
本文探讨了对比学习在工业互联网中用于隐私保护的应用，分析了其在工业数据处理中的独特优势和未来研究方向。
摘要：
工业互联网（IIoT）将智能传感、通信和分析整合到工业环境中，包括制造、能源和关键基础设施。尽管IIoT能够实现预测性维护和现代工业控制系统（如制造和能源领域）的跨站点优化，但其对操作数据的敏感性也引入了显著的隐私和保密风险。对比学习作为一种自监督表示学习范式，最近 emerged 作为一种有前景的方法，通过减少对标记数据和原始数据共享的依赖，用于隐私保护分析。虽然基于对比学习的隐私保护技术已在物联网（IoT）领域进行了探索，但本文提供了专门针对工业互联网（IIoT）系统隐私保护的全面综述。它强调了工业数据的独特特性、系统架构和各种应用场景。此外，本文讨论了解决方案和开放挑战，并概述了未来的研究方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
