# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 5 Discussions
### Implications  
This study conducted a comprehensive analysis of 16 privacy-preserving attributes, which were categorized into five groups. The research on privacy protection in cloud storage is an ever-evolving field, and this paper highlights certain limitations in existing techniques. These limitations include a reliance on a single cloud service provider (CSP), insufficient reporting of key management attributes, and a lack of consideration for active threats. Furthermore, significant variations were observed in key length, external assets, irregularity features, and the software and hardware components used in testing. These findings provide valuable insights for future research in the field of cloud storage privacy.  
Several critical factors identified in the literature contribute to the development of efficient privacy-protecting techniques for cloud storage. It is crucial for these techniques to be adaptive and cohesive, capable of withstanding various privacy breaches. Incorporating an internal auditing procedure is recommended as it is more reliable and efficient compared to relying solely on third-party auditors, which may introduce additional risks to cloud services. Cryptographic techniques utilizing asymmetric cryptography mechanisms, which involve the use of multiple keys for data encoding and decoding, are preferred due to their reliability and dependability. Encrypting critical data at every stage of transfer is essential when utilizing cloud storage to ensure data privacy, and iterative encryption can effectively prevent CSPs from tampering with sensitive information.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Paper ID** & **Advantages** & **Limitations** \\ \hline \multirow{4}{*}{6.} & [38] [PERSON] et al. (2022) & It includes advanced use of hierarchical deep learning for scalable and effective threat detection. Further, it ensures healthcare data integrity. & It focuses mainly on healthcare use cases; it has limited discussion on anonymization and cryptographic details. \\ \hline \multirow{4}{*}{7.} & [41] [PERSON] et al. (2019) & It provides an innovative CP-ABE scheme that ensures robust access control and partial anonymization. It is scalable with IoT-cloud integration. & It has limited focus on auditability and broad threat landscapes; it mainly addresses attribute-guessing attacks. \\ \hline \multirow{4}{*}{8.} & [55] [PERSON] et al. (2019) & It provides a comprehensive review of cryptographic challenges and solutions. It emphasizes data integrity. & It focuses primarily on cryptographic methods; it has scalability challenges in multi-cloud contexts. \\ \hline \multirow{4}{*}{9.} & **The proposed multi-layer encoding framework** & **Incorporates various privacy-protecting attributes traced in the survey. Further, it incorporates well-designed elements to ensure data privacy and security.** & **To ensure cloud scalability within the framework, it is imperative to establish a viable mechanism.** \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._Scalability is a significant attribute to consider in privacy-preserving techniques, particularly in the context of cloud computing (CC), where cloud server resources may be limited. However, using scalability features in privacy-preserving techniques can sometimes be inefficient or counterproductive due to the prevailing privacy concerns in CC. Platform as a service (PaaS), achieved through the expansion of database replicas, and infrastructure as a service (IaaS), accomplished by increasing the number of load balancing virtual machines, are two methods proposed in the literature to address scalability in CC services. Some privacy-preserving techniques, such as HASBE and MapReduce, have incorporated scalability features to enhance their effectiveness.