## 2 Related Work
The prime contributions of the work were as follows: (i) despite considering machine learning, it utilized the sensitive attribute-based classification; (ii) the encryption was performed for sensitive data; (iii) the encryption time was reduced without affecting the data quality; (iv) the decryption took place with the required groups. Lastly, the simulation results were validated and analyzed with heterogeneous parameters such as the processing time, classification rate, security level, and storage space. Thus, the suggested work yielded better results in ensuring data privacy and data security when storing cloud data. It also achieved less cost for processing the functionalities. [PERSON] et al. [30] presented an Artificial-General-Intelligence-based Rational-Behavior-Detection Agent (AGI-RBDA) for tracking online harm. They used various cognitive correlates such as intention, perception, motivation, emotions, and implicit and explicit knowledge to protect sensitive information like a human mind. They exposed and stimulated the behavior of different cognitive correlates and compared them with the encryption techniques used in expert systems. The benefit of this method is that it can analyze the intruder's behavior and overcome the encryption weaknesses in expert systems. The limitation of this method is that it may require high coordination between artificial intelligence and cognitive correlates. [PERSON] et al. [31] presented a Network Functions Virtualization (NFV) for mobile core and heterogeneous cellular networks. They used a Software-Defined-Network-based Network Functions Virtualization (SDN-NFV) architecture to improve the performance and reliability of the Mobility Management Entity (MME) in 5G wireless communication systems. The benefit of this method is that it can reduce the cost and complexity of the MME deployment and enhance its scalability and flexibility. The limitation of this method is that it may require high coordination between the SDN controller and the MME endpoints. Similarly, [PERSON] et al. [32] presented a green communication framework for Wireless Body Area Networks (WBANs). They used an Energy-aware Link-Efficient routing approach (ELR-W) to select the best nodes for data transmission based on their energy level and the link quality. [PERSON] et al. [33] discussed the importance of achieving energy efficiency in cloud computing. The authors presented a taxonomy of cloud computing energy/power management techniques and reviewed existing techniques based on a tool, OS, virtualization, and data center stages taxonomy. The benefits of energy-saving techniques include reduced energy consumption, cost savings, and improved sustainability. However, the limitations include the need for accurate modeling and monitoring of energy consumption, as well as the potential impact on performance and reliability. However, the same author in [34] provided a comprehensive analysis of the integration of Wireless Sensor Networks (WSNs) with cloud and fog computing. The benefits of the integration include the improved scalability, efficiency, and reliability of WSNs, as well as the availability of cloud and fog resources for data storage and processing. They also explored the integration of WSN/IoT with Fog Computing (FC) and found that WSN integration with FC has many benefits with respect to latency, energy consumption, data processing, and real-time data streaming. However, the WSN network still had some limitations in computing power,storage resources, and battery life, which made the network restricted with respect to the data transformation. [PERSON] et al. [35] proposed an optimized communication scheme for an energy-efficient and secure Flying Ad hoc Network (FANET) using drones and Unmanned Aerial Vehicles (UAVs). They used a Whale Optimization Algorithm based on Optimized Link State Routing (WOA-OLSR) over the FANET to provide optimal routing for energy-efficient and secure data transmission. [PERSON] et al.