Trust-Based Geo-Social Access Control Framework: Employee Management through Machine learning Techniques

In the modern corporate sphere, organizational trustworthiness is crucial, particularly in understanding and predicting employee behavior through data science. Managing employee access levels is vital for security and privacy. Our study introduces a novel approach, utilizing Geo-Social and Trust data. Various machine learning techniques, including Linear Regression, K-Nearest Neighbours, Decision Tree, Random Forest, XGBoost, and Multi-Layered Perceptron, are employed to extract insights from geographical and social sources. This extensive dataset contains detailed geographic employee information, navigation choices, spatial exploration patterns, and choice set formations. Grounded in this blend, we establish an access control data modeling system to evaluate employee access based on trustworthiness. This model categorizes employees into low, moderate, high, and very high access levels, adapting to changing behavior. Our method fosters a cohesive, trustworthy employee network and provides a precise mechanism for assessing trustworthiness. It promises augmented organizational coherence, heightened commitment, and reduced attrition. Additionally, this approach safeguards against data breaches and privacy violations by restricting access for less trustworthy employees. In summary, our innovative access control framework, merging Geo-Social and Trust data with cutting-edge machine learning, heralds a new phase of trust, precision, and security in organizational dynamics.