An Identity Verification Governance Public Data Access Model for Analyzing Public Data Access Governance using Artificial Neural Network

The main objective of this paper is to design and implement AI algorithms for public data access governance. An identity Verification Governance Data Access (IVGDA) model uses the ANN algorithm to secure public data access governance. The ANN algorithm is implemented to address and analyse the problems of public data access governance, including computational complexity, data security, and management. Most public sectors, organizations, and institutions face many problems balancing transparency in security, privacy, and scalability for diverse datasets. The ANN algorithm proposed here leverages different datasets of regular guidelines, data patterns, and interactions with multiple people connected in public data applications. Since public data is transferred globally, it is essential to transmit with balanced privacy, transparency, and effective governance. This paper explains the efficiency of AI algorithms in creating a high-level security-based governance public data accessing model. A simulation is created in a Microsft Active Directory. The proposed IVGDA model is deployed to examine and evaluate the memberships and authorities of the users involved in the directory. The ANN analyses the account, profile, access rules and roles, data models, and other parameters, and the output is collected. Compared with the existing models, the IVGDA model establishes significant governance and outperforms others. In the simulation, various processes like user addition, deletion, group creation, alteration in the group, profile and all are used to evaluate the performance of the IVGDA model. Additionally, this paper contributes to data governance by providing an advanced computing algorithm for understanding and optimizing public data access governance.