A hybrid boosted neural sensitive attribute detection machine learning algorithm for HABAC systems

The sensitive attribute selection requires a well-trained machine-learning model to avoid unauthorized access to sensitive data. A new hybrid approach Boosted Neural Sensitive Attribute selection (BNSAD) method is used to extract sensitive attributes from the authorized data. This method is used in various access control methods mainly our proposed Hierarchical Attribute Based Access Control (HABAC) system. The HABAC gives data privacy by ensuring authorized individuals or groups only access the data. The main aim of the proposed system is to give a new hybrid machine-learning algorithm for data privacy. The BNSAD algorithm identifies the patterns from data and gives the boosted trained model as an input to the neural network to predict the sensitive attribute. The performance of seven machine learning algorithms was identified initially. The top three performance metrics of the algorithms taken for the new algorithm construction. The 94%, 92%, and 90% accuracy of the BNSAD algorithm for three datasets is higher than the existing three selected top three algorithms. The algorithm is implemented on BLP, RBAC, ABAC, and HABAC access control models. The final model accuracy of 98%, 98%, and 95% is achieved by BNSAD with HABAC for three medical datasets to identify the sensitive attributes for enhanced privacy of the data. The performance metrics for the proposed HABAC system are high with the existing models.