# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 2 Background
### Related Work  
Cloud computing (CC) is widely recognized as the foundation of future innovative research [22; 23]. However, data privacy has emerged as a threat to CC services in various forms. Over the past decade, researchers have focused on developing privacy-protecting approaches to alleviate these data threats. Initially, frameworks and strategies were developed without a comprehensive understanding of the diverse range of threats present in cloud storage [24]. To gain insights into these threats, several survey studies have been conducted, categorizing privacy threat types in cloud storage [24; 25; 26; 27]. These categorizations include commonly highlighted attributes such as encryption, access control, and auditing. For example, in [25], integrity checking was employed to protect cloud storage privacy. Ensuring the reliability of data during transfer is of utmost importance, and integrity checking serves as a vital mechanism in achieving this. By comparing the data to a previous version, integrity checking provides conclusive evidence of its validity. Keyword search, on the other hand, enables users to encrypt textual documents and perform searches for specific keywords or phrases while preserving privacy [28]. This novel approach involves locally encrypting the data before transmitting it to the cloud. To address the challenge of data privacy when retrieving information from cloud storage, remote data integrity checking protocols have emerged as promising technologies. These protocols verify the privacy and integrity of data during retrieval, ensuring that it remains protected from unauthorized access [29]. In a related study, a methodology called dynamic metadata reconstruction was developed to efficiently organize metadata in cloud storage databases. This methodology aims to enhance the overall management and organization of data in cloud storage systems [30]. SecuDB is a secure relational database built on Intel TDX. It offers multi-level privacy and tamper resistance in cloud-based environments. Further, it uses advanced masking techniques and secure temporal tables to protect data from online theft and attacks. It maintains up to 94.70% performance with minimal overhead by ensuring efficient and verifiable operations for contemporary cloud-based workloads [31]. Hence, the categorization and exploration of privacy threat types in cloud storage are crucial steps towards developing robust privacy-preserving techniques. By identifying and understanding these threats, we can devise comprehensive strategies that effectively address privacy concerns. The use of encryption, access control, and auditing measures demonstrates the importance of incorporating multiple layers of protection into cloud storage systems. Moreover, the emergence of innovative approaches like dynamic metadata reconstruction and remote data integrity checking highlights the continuous evolution of privacy-preserving techniques. As the cyber landscape evolves, it is essential to explore new methods and technologies to stay ahead of emerging threats and ensure the privacy of data stored in the cloud. By considering a wide range of attributes and approaches, we can enhance the effectiveness and adaptability of privacy-preserving measures, making cloud storage a secure and trusted environment for data storage and retrieval. Data anonymization is another technique that involves encoding or modifying user data to prevent the disclosure of the data owner's identity. The authors [PERSON] et al. [32] applied multi-layered security frameworks and offered innovative strategies to reinforce data privacy within cloud-based environments. Their study introduced a hierarchical federated learning-based approach for tracing anomalies in industrial IoT systems. The research work of [33] combined blockchain concepts with edge computing technologies to permit secure data aggregation in IoT networks. It included the description of encryption, decentralized validation, and tamper-proof storage approaches to secure sensitive data during collection and processing.