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Contrastive Learning for Privacy Enhancements in Industrial Internet of Things

arXiv 2602.00515 ↗First posted 31 Jan 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 47 references that are in the map. First posted on arXiv 31 Jan 2026.

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References · 24

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
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12 more
  1. 2019Hidden Trigger Backdoor Attackscited by 359
  2. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
  3. 2018Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learningcited by 75
  4. 2023InfoNCE Loss Provably Learns Cluster-Preserving Representationscited by 37
  5. 2021Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacycited by 14
  6. 2024Backdoor Contrastive Learning via Bi-level Trigger Optimizationcited by 14
  7. 2024CleanerCLIP: Fine-grained Counterfactual Semantic Augmentation for Backdoor Defense in Contrastive Learningcited by 13
  8. 2025PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environmentscited by 9
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  11. 2025Log Anomaly Detection with Large Language Models via Knowledge-Enriched Fusioncited by 8
  12. 2018User Perceptions of Smart Home IoT Privacycited by 5

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