Privacy-Preserving Machine Learning Models for Industrial IoT Devices

EasyChair Preprint 13620

Abstract

Industrial Internet of Things (IoT) devices have revolutionized the way industries operate by enabling real-time monitoring, predictive maintenance, and process optimization. However, the widespread adoption of IoT devices also raises concerns about data privacy and security. As these devices collect and transmit sensitive data, protecting the privacy of industrial data becomes crucial. Privacy-preserving machine learning models offer a promising solution to address this challenge. This paper presents an overview of privacy-preserving machine learning models specifically designed for Industrial IoT devices. We discuss the unique challenges faced in preserving privacy in this context, including limited computational resources, communication constraints, data heterogeneity, and security risks. Various techniques such as differential privacy, federated learning, homomorphic encryption, and secure multi-party computation are explored for privacy preservation. We propose a privacy-preserving machine learning framework tailored for Industrial IoT devices, covering data preprocessing, model training, aggregation, and deployment phases. Evaluation metrics for assessing privacy guarantees, accuracy, performance, and communication overhead are also discussed. Furthermore, we present case studies and applications where privacy-preserving machine learning has been successfully applied, such as predictive maintenance, anomaly detection, and quality control in industrial processes. Considerations for model deployment, including security measures and compliance with regulations, are highlighted. Lastly, we outline future research directions and challenges, including scalability, communication optimization, adversarial attacks, and standardization efforts. This paper emphasizes the importance of privacy-preserving machine learning models for Industrial IoT devices and their potential to ensure data privacy while enabling intelligent decision-making in industrial settings.

Keyphrases: data privacy, dvanced privacy techniques, machine learning, privacy preserving, secure storage, sensitive dataa

@booklet{EasyChair:13620,
  author    = {Godwin Olaoye},
  title     = {Privacy-Preserving Machine Learning Models for Industrial IoT Devices},
  howpublished = {EasyChair Preprint 13620},
  year      = {EasyChair, 2024}}