###### Abstract
In addition, it helps to check the integrity before adding them to the genesis because the existing ABAC and RBAC systems have low efficiency, and these are not based on learning-based methods [13];
5. Finally, we achieve more efficiency and security. Our proposed approach will examine the parameters chosen, including user behavior, attributes, trust, unauthorized request, forbidden request, and range of specification. Users will be divided into different categories based upon the trust value such very low, low, unknown, moderate, high, and very high trusted users. A threshold value will be set, i.e., if a user meets the threshold value and also meets the policy, then access will be granted. In this section, we provide details and preliminaries in order to proof the application of AI in blockchain and homomorphic encryption. Moreover, our proposed system provides trust to several parties and individuals withing the consortium's blockchain. Our main motivation is to design a blockchain based secure searchable framework that uses deep learning techniques in order to build a trust. Finally, our goal is to build a generic DL-based secure searchable model that can support several different types of AI scenarios and different types of datasets. In order to obtain the scalability and reliability in cloud-based healthcare data sharing schemes illustrated in [14], the schemes have been designed in the literature by implementing the encryption and operation anonymization. Moreover, participants are always curious to transfer their private and sensitive data to the cloud due to the potential risks [15]. For example, the medical industry can also benefit from the industrial IoT (IIoT), where industrial sensors and actuators are used as wearable devices to collect users physiological data, such as blood pressure, electrocardiogram (ECG), temperature, and so on. These physiological data are usually sent to the nearby devices or servers of the user to carry out further data collection, aggregation, and then sent for diagnosis and input to an industrial provider using an open channel, i.e., the Internet. However, due to the round-the-clock networking of nodes in this IoT network, it is vulnerable to various security issues, such as message tampering, eavesdropping, and denial of service (DoS) attacks. In the industrial industry, this raises major security issues as the misuse of data can result in the incorrect diagnosis and can cause life-threatening scenarios for the patients under observation. Privacy issues are often related to compromising sensitive information by using active and passive attacks. In active attacks, the adversary aims to obtain access, infer, and/or alter private data. Recently, several research works related to data privacy protection and identification of cyber-attacks particularly using cloud and edge computing integrated with blockchain are proposed to tackle the above issues. However, it should be noted that there still exist demerits in the system. The major challenges include designing a privacy-preservation scheme to protect the sensitive data transactions against being accessed by unauthorized users [16]. Moreover, ensuring a secured authentication data transfer scheme and maintaining data integrity when communicating the data over an IoT network is a challenging issue. Second, designing an adaptable security mechanism that can efficiently distinguish normal and attack instances in IoT is also a challenging issue. As such, the IoT network comprises various interconnected medical sensors, actuators, and machines (e.g., VMs and platforms), located at multiple locations [17]. Third, developing a new framework for deploying blockchain and deep learning techniques in current cloud-edge-assisted industrial systems is strenuous as such frameworks often face issues related to scalability. Moreover, due to the different computing powers of the participating edge nodes, it is infeasible to store the complete block in the edge networks [18].