Privacy-Preserving of Digital 6G IoT Based Cyber Phycical System in Medical Big-Data Application Using Homomorphic Encryption

This abstract introduces a novel approach to privacy protection in cyber-physical systems built on the 6G Internet of Things (IoT), with an emphasis on applications involving medical big data and the use of homomorphic encryption. The proposed method incorporates a complex three-step procedure—sensitivity assessment, polynomial interpolation with noise calibration, and perturbed data generation—to address the challenging trade-off between privacy and utility. The framework guarantees the security of sensitive medical information while making the most of the data by maintaining the original dataset's geographical structure and using differential privacy standards. The suggested approach outperforms conventional perturbation processes in experimental evaluations, displaying improved privacy protection and low time consumption. In order to preserve the original data distribution, the study stresses the need of choosing suitable privacy budgets (ε values) that are customised to meet the demands of each application. Encouraging safe and effective data processing in healthcare settings, the framework is a giant leap forward in resolving privacy issues in the ever-changing world of digital 6G IoT-based cyber-physical systems, especially as they pertain to medical big data applications.