Explore the fascinating world of Industry 5.0 and its emphasis on human-centricity! Discover how collaboration between humans and intelligent systems, like cyber-physical systems, is revolutionizing smart factories. Learn about the essential privacy and security measures needed to maintain trust, prevent data breaches, and ensure safe operations. Delve into the importance of safe machine learning predictions and the critical role of batch verification in enhancing security. Understand how homomorphic encryption protects sensitive data, enabling secure analysis and collaboration. Join us as we unveil these groundbreaking technologies shaping the future of industry!

We have a top Q1 paper published with IEEE Trans on Industrial Informatics some time ago but thought it's better to publish a video to demonstrate its impact. It's FREE. Please check [ Ссылка ] or
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Overview:
00:00:00 A Secure and Private Future
00:01:07 Where Humans and Machines Converge
00:02:26 Ensuring Trust in Predictions
00:03:32 Computation on Encrypted Data

Abstract:
As a highly integrated industrial system, human cyber-physical systems (HCPSs) provide accurate and high-quality services for Industry 5.0. In HCPSs, machine learning (ML) prediction provides reliable prediction results for users based on matured models, while security and privacy protection are considerable issues. In this article, based on the modified Okamoto–Uchiyama homomorphic encryption, we propose a verifiable privacy-preserving machine learning prediction scheme for the edge-enhanced HCPSs, which outputs the verifiable prediction results for users without privacy leakage. Specifically, a batch of prediction results can be verified at one time, which improves the efficiency of verification. Security analysis shows that our scheme protects the privacy of inputs, ML model, and prediction results. The experiment results demonstrate that the edge computing architecture remarkably alleviates the computational burden of the cloud server. Furthermore, compared with other related schemes, our scheme shows the best execution efficiency, and batch verification optimizes the performance by about 15% compared with single verification on the same scale.

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