Machine Learning-Based Detection of Data Privacy Vulnerabilities in Critical Smart Warehouse and Distribution Systems

Abstract

The rapid adoption of Internet of Things (IoT) devices, autonomous mobile robots, facial recognition surveillance systems, and cloud-integrated warehouse management platforms in U.S. smart warehouse and distribution facilities has created a complex and largely under-analyzed attack surface for data privacy violations. Worker biometrics, inventory records, supplier transactions, and operational telemetry are now generated and processed at scale across interconnected systems that span physical, network, application, and cloud layers. This article presents a comprehensive examination of machine learning-based techniques for detecting data privacy vulnerabilities in these environments, integrating findings from the latest research in deep learning intrusion detection, adversarial robustness, federated privacy-preserving learning, blockchain-secured IoT data sharing, and computer vision security. We analyze the threat landscape through five system layers, evaluate the performance of leading ML detection frameworks including WILS-TRS, TAD, FedPrIDS, and CNNbased vision audit systems, and examine their alignment with the U.S. regulatory landscape including CCPA, BIPA, NIST CSF 2.0, and Executive Order 14028. A phased implementation roadmap is proposed to guide warehouse operators from baseline intrusion detection to fully federated, blockchain-anchored privacy governance. Our analysis demonstrates that while no single ML paradigm addresses all vulnerability classes, a layered deployment combining federated learning, adversarial training, and blockchain data provenance offers the most robust and privacy-preserving solution for modern U.S. distribution infrastructure.