# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 3 Privacy-Protecting Attributes Classification for the Storage of Cloud Data  
This subdivision of the research paper focuses on addressing the research question and aims to identify the essential privacy-protecting attributes in cloud data storage. Developing a privacy preservation framework requires a thorough consideration of all relevant attributes. To achieve this, various extensive reviews of the literature and other contributions were conducted, utilizing multiple databases and search phrases for locating studies that discuss privacy preservation in cloud data storage. The compilation of privacy-protecting attributes from these studies covers various aspects and is divided into several categories. These attributes represent the latest advancements in cybersecurity and encompass different dimensions of privacy preservation [40].  
Design management includes features related to auditing techniques, cryptographic approaches, exterior assets, and the structure of the cloud storage. Key management focuses on mechanisms for the generation of keys, the length of the keys, governance, and various functions. The test management function involves the test environment and the applied tests to ensure privacy [41]. The threat management function addresses different types of threats and the measures taken to mitigate them. The performance management function encompasses standards, abnormality detection, and achievements in privacy. In addition to discussing these privacy attributes, this paper incorporates recent studies that highlight innovative approaches to tackle cybersecurity challenges. For instance, [PERSON] [42] proposed an image steganography technique for adversarial network architecture.  
Furthermore, [PERSON] [43] introduced a new technique for detecting compressed deepfake videos, which have become a significant concern. Similarly, [PERSON] et al. [44] present an SNIS (Swiss Network for International Studies)-based approach for forgery detection in post-processed images. In the upcoming sections, this paper will delve into these privacy-protecting attributes (descriptions in Figure 1) and explore the findings of these innovative studies. By incorporating the attributes in Figure 1 and advancements, the aim is to provide a complete understanding of privacy preservation in cloud data storage and offer insights into potential solutions and strategies for enhancing data privacy in the cloud.  
LedgerDB is an advanced database system used to enhance trust, auditability, and data verification in the modern era. It is a centralized ledger database that mainly focuses on ensuring transparent and verifiable data operations. It includes cryptographic structures, e.g., Merkle trees, to link the security of records. Therefore, it identifies any unauthorized changes in databases. Further, it permits the users to audit data history efficiently and  
Figure 1: Privacy-protecting attributes taxonomy.  
supports integrity checks, without the help of system administrators, using a sequential and tamper-resistant design.  
On the other hand, the VeDB has the capability to integrate software techniques and hardware-based security to convey a trusted relational database platform. It uses a trusted execution environment, e.g., Intel SGX, for safeguarding computations within secure hardware zones. Hence, it prevents unauthorized access to the system software. Here, remote attestation mechanisms are engaged to verify and validate the integrity of the secure zones. These advanced database technologies display a shift toward combining strong assurance models with conventional database capabilities. LedgerDB and VeDB provide high-assurance applications with enhanced security without compromising performance or usability by embedding cryptographic authentication and hardware-based protection. These database technologies are used in writing queries in structured query languages.