## 2 Background Review  
Multilevel security employs a strategy that classifies data and users using a combination of hierarchical security levels and non-hierarchical security categories [11]. According to [12], a multilevel security policy has two main objectives. The controls must first prevent unauthorized users from accessing data classified above their acceptable level. Secondly, they must restrict entities from declassifying data. In a multilevel secure system, when a subject attempts to access an object, the system must decide whether to grant access. For example, [PERSON] et al. proposed a multilevel privacy protection approach to mitigate data compromise attacks on private cloud systems [13]. Additionally, other studies have focused on developing and deploying a functional prototype of a multilevel secure database system [14]. [PERSON] focused on designing a multilevel blockchain architecture to uniformly distribute operations [15]. [PERSON] et al. also proposed an algorithm for image encryption using a multilevel permutation technique [16]. Some models are designed to integrate with distributed systems, providing a set of security patterns for large, complex systems [17]. The intelligent system's multilevel security policy model has been examined as a guide for its main application, which is building dependable distributed systems [18]. In another study, the use of a tool designed for users improved the instructional efficacy of teachers in presenting material [19]. In publication [20], the authors present a framework that addresses the security concerns of private cloud systems. The implementation of the multilevel secure database prototype was carried out using the Microsoft SQL code program. However, performance evaluation experiments were conducted utilizing the implemented prototype [21]. Additionally, the investigations examined various aspects of multilevel security in distributed database management systems. These aspects include system design, security policy, and data/metadata distribution challenges in the context of the MLS. Some inquiries have explored the architectural considerations and principals involved in developing a secure database system [22; 23]. The current robust and secure Medium Access Control (MAC) standards have provided effective and anticipated defense against channel attackers [24]. In artificial intelligence, the homomorphic encryption-based deep learning secure searchable blockchain system functions as a distributed database, allowing users to securely access data through searches [25]. Several strategically positioned smart contacts, including multiple access control intelligent contracts, have been used as part of the trusted and secure system that guarantees the implementation of these functionalities [26]. As highlighted in the previous section, some models in healthcare use techniques to preserve healthcare information securely. These processes ensure compliance with the security and privacy requirements of the Health Insurance Portability and Accountability Act (HIPAA) [27]. Most studies have focused on performance and security mechanisms, such as scalability and availability. Building on this prior work, our research integrates and justifies the development of an innovative model by incorporating cryptographic security algorithms. Additionally, another approach has enabled fine-tuned access control across various partnership levels, creating a trusted platform for data sharing. It also designs an automated system to determine these partnership levels [28]. In study [29], the authors explore enhancing multilevel security using latent semantic indexing. This approach simplifies the management of multilevel security-based operating systems while enabling the effective utilization of extensive security measures. [PERSON] et al. propose a fast and efficient method that requires minimal computation while effectively suppressing noise and improving accuracy.