EiAiMSPS: Edge Inspired Artificial Intelligence-based Multi Stakeholders Personalized Security Mechanism in iCPS for PCS

Artificial Intelligence (AI) is becoming more prevalent in the healthcare sector like in pharmaceutical care to achieve rapid and precise outcomes. Machine learning techniques are critical in preserving this balance since they ensure both the confidentiality and authenticity of healthcare data. Early sickness projections benefit clinicians when establishing early monetary choices, in the lives of their patients. The Web of Things (IoT) is acting as an accelerator to boost the efficacy of AI applications in healthcare. Healthcare service pharmaceutical care is also in demand and can have AI for good patient care. The sensor gathers the data from individuals, then the data is examined employing machine learning algorithms. The work’s major intent is to come up with an automated learning-based user authentication algorithm for providing secure communication. The other goal is to ensure data privacy for sensitive information that does not currently have security. The Federated Learning (FL) technique, which uses a decentralized environment to train models, can be utilized for this purpose. It enhances data privacy. This work proposes in addition to security a differential privacy preservation strategy that involves introducing random noise to a data sample to generate anonymity. The model’s performance and data quality are assessed, as privacy preservation approaches frequently reduce data quality.

@article{Devliyal2024,
  title     = {EiAiMSPS: Edge Inspired Artificial Intelligence-based Multi Stakeholders Personalized Security Mechanism in iCPS for PCS},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Swati Devliyal and Sachin Sharma and Himanshu Rai Goyal},
  doi       = {10.14569/IJACSA.2024.01508117},
  url       = {https://doi.org/10.14569/IJACSA.2024.01508117}
}