Risk Inference-Based Privacy Preservation Model for Cyber-Physical Systems

Abstract

The Risk Inference-Based Privacy Preservation Model for Cyber-Physical Systems (CPS) addresses the critical need for safeguarding privacy in the evolving landscape of interconnected physical and digital environments. This model, aptly named RIP2 (Risk Inference for Privacy-Preserving CPS), integrates advanced risk assessment techniques with robust privacy-preserving mechanisms to create a dynamic and adaptive framework. The model begins with a comprehensive risk assessment module that identifies potential threats, values privacy-sensitive assets, and assesses vulnerabilities within the CPS architecture. A privacy risk inference engine dynamically analyses contextual data, user behavior, and continuously evolving risk factors to assess the current privacy risk level. Privacy-preserving mechanisms, including differential privacy, encryption, and anonymization, are adaptively applied based on the inferred risk level, ensuring a tailored and effective approach to privacy preservation. Users are empowered to define their privacy preferences, and the model incorporates dynamic privacy policies that automatically adjust based on the risk assessment. Furthermore, the model incorporates incident response and continuous learning mechanisms to respond promptly to privacy incidents and improve the overall resilience of the system. The RIP2 Model aims to strike a balance between the seamless functionality of CPS and the paramount importance of preserving individual privacy in an interconnected and data-driven world.