Encrypted AI for Predictive Maintenance in Industrial IoT

Abstract

In the realm of Industrial Internet of Things (IoT), predictive maintenance plays a pivotal role in optimizing operational efficiency and minimizing downtime. However, the sensitive nature of data involved in predictive maintenance poses significant challenges in terms of privacy and security. This abstract explores the integration of encrypted artificial intelligence (AI) techniques to address these challenges effectively. Encrypted AI leverages advanced cryptographic protocols to enable the analysis of sensitive industrial data while preserving its confidentiality. By employing techniques such as homomorphic encryption and secure multi-party computation, encrypted AI allows computations on encrypted data without exposing the raw information to unauthorized parties. This approach facilitates the application of sophisticated machine learning models for predictive maintenance tasks without compromising data privacy.