Privacy-Preserving AI Analytics for Industrial IoT Data: Techniques and Protection

EasyChair Preprint 13288

Abstract

The advent of the Industrial Internet of Things (IIoT) has facilitated the collection and analysis of vast amounts of data from diverse sources within industrial settings. However, the sensitive nature of this data raises significant privacy concerns. To address these concerns, privacy-preserving techniques and protection mechanisms have emerged as essential components of AI analytics in the context of IIoT.

This paper provides an overview of privacy-preserving AI analytics techniques, specifically tailored for industrial IoT data. The core objective is to enable organizations to extract valuable insights from their data while ensuring the privacy of sensitive information.

The paper begins by discussing the unique privacy challenges associated with industrial IoT data. These challenges arise due to the inclusion of diverse data sources, such as sensors, machines, and control systems, which capture sensitive information related to operations, processes, and equipment. Moreover, the distributed and interconnected nature of IIoT environments further complicates data privacy concerns.

Next, the paper explores various privacy-preserving techniques that can be employed in the context of AI analytics for industrial IoT data. These techniques include secure multi-party computation, homomorphic encryption, differential privacy, and federated learning. Each technique is described, highlighting its strengths and limitations, as well as its suitability for different IIoT scenarios.

Keyphrases: AI analytics, Industrial IoT, Privacy challenges, Techniques, data privacy, data protection, privacy preserving

@booklet{EasyChair:13288,
  author    = {Ayuns Luz},
  title     = {Privacy-Preserving AI Analytics for Industrial IoT Data: Techniques and Protection},
  howpublished = {EasyChair Preprint 13288},
  year      = {EasyChair, 2024}}