###### Abstract  
One of the industries with the fastest rate of growth is healthcare, and this industry's enormous amount of data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. However, maintaining privacy and security in an untrusted green cloud environment is difficult, so the data owner should have complete data control. A new work, SecPri-BGMPOP (Security and Privacy of BoostGraph Convolutional Network-Pinpointing-Optimization Performance), is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. The Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, was first applied to the input dataset to begin the clustering process. Second, it was enlarged by employing a piece of the magnifying bit string to generate a safe key; pinpointing-based encryption avoids amplifying leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid Fragment Horde Bland Lobo Optimisation (HFHBIO). Our proposed method is currently kept in a cloud environment, allowing analytics users to utilise it without risking their privacy or security. big data; security; privacy; Boost Graph Convolutional Network Clustering algorithm; magnify pinpointing based encryption approach; Hybrid Particle Swarm; Grey Wolf Optimization 25 June 2024 202  
other Internet of Things devices gather health data. The medical IoT network gathers physiological data, which is then sent to the healthcare big data centre for archival and disease diagnostics. The medical file must be encrypted before transmission in order to preserve patients' privacy and stop third parties from listening in on private communications. On the protected data, the patient applies an access policy to define the permissible attributes and relationships. Only users possessing the appropriate attribute secret keys--such as a doctor, nurse, anaest, or a patient's family--are allowed to decrypt the ciphertext. Attribute-based encryption is the name of this encryption technique [2]. Information security and protection have evolved into a fundamental problem that impacts numerous cloud applications. The ease with which cloud administrators can access sensitive data is one of the major concerns with regard to data security and privacy. This concern sharply increases client anxiety and hinders the adoption of distributed computing in many industries, including the financial sector and governmental and administrative entities. The security of structured or unstructured data is not addressed by the conventional big data approach. In cases where the risk of disclosing personal information is real, it also has to combine the concepts of insurance and security. The enormous combination of organised and big data necessitates new models for improving safety and security. The growing issue of data security in big data is of great interest to researchers [3]. A patient with unclear symptoms may be diagnosed and treated in many hospitals, and various medical records may be kept, according to the current medical system [4]. As a result, developing a cross-domain, safe data-sharing system is essential to facilitating patient care at various hospitals. For instance, the clinicians at hospital B have access to the examination report created by hospital A. The public clouds are used to store and provide ubiquitous data access for the encrypted medical files produced by various hospitals [5]. The cross-domain access policy for a patient's protected medical records is determined by the patient.