Vulnerability Assessment of AI-Based Privacy Mechanisms in Industrial IoT Consumer Systems

The secure development of consumer systems with Artificial Intelligence (AI) strength within the Industrial Internet of Things (IIoT) has gotten more obscure. These systems are being dependent on sensitive information and are secured with state-of-the-art Privacy-Preserving Machine Learning (PPML) methods like Federated Learning (FL). But there are still weaknesses at the consumer side, and these are largely involving mobile companion applications that manage the IIoT units. This leads to a feedback loop in vulnerability where protection of the model can be broken through the vulnerability of consumer-sensitive software. To counteract these interrelated risks, the new proposed model, the Robust Privacy-Preserving Vulnerability Assessment Model (RPVAM), presented in this paper, provides a holistic risk assessment of systems. Federated Defense Mechanism (FDM) after robust aggregation, and Privacy-Enhanced Federated Defense Mechanism (PE-FDM) after Local Differential Privacy (LDP) are two defense strategies that are tested against each other in this framework. Attacks of data poisoning are tested in different strengths. It is shown that the two mechanisms perform more strongly than the standard FL in terms of utility and robustness in the face of an attack. It also shows the trade-offs that will always exist between the three dimensions of balancing: privacy, robustness, and utility, and explains the shortcomings of the existing siloed-based approaches. It is suggested that the paradigm change of security evaluation practice should be proposed and that the idea of unified models is in respect of the entire data lifecycle subscribing to the consumer endpoint through to the AI core.