Optimal Key Generation using Support Vector Machine for Health Records
Himanshu, Akash Punhani · 2024
As the digital age continues to progress, the demand for advanced data privacy techniques has become increasingly vital. With the rise of large-scale collection of data and the proliferation of machine learning algorithms, ensuring privacy while extracting valuable insights has become a challenging situation. This research paper proposes an approach that leverages machine learning-based techniques to achieve scalable privacy preservation, while also providing an optimal key generation mechanism for data encryption and decryption. The proposed approach is evaluated on a real-world healthcare dataset, and the experimental results demonstrate that the proposed approach provides a significant improvement in privacy preservation, while maintaining the performance and accuracy of the system. Healthcare data is among most sensitive one for this scenario as this data encompasses a vast array of information related to patient health, medical conditions, treatments, outcomes, and more. Leveraging this data in a responsible and privacy-preserving manner holds tremendous potential for improving healthcare outcomes, advancing medical research, and enhancing the overall quality of patient care.