# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 4 The Anticipated Cloud Storage Structure
### Contributions and Improvements in Existing Ideas  
In 2011, [PERSON]\({}^{\prime}\)an [70] introduced a cloud storage framework and developed an interaction protocol among participants. The diagram in Figure 4 illustrates the functional modules of users, data owners, and storage service providers, along with their interactions. The dashed lines indicate the functional or structural correspondence between the connected components. This privacy-preserving cloud storage framework incorporates an interactive protocol, multi-tree index, EKDA (_extirpation-based key derivation algorithm_) for key management, and a DWBF (_double-hashed and weighted Bloom filter_) to address changes in user access rights and dynamic data operations. The proposed multi-layer encoding framework of data privacy in cloud storage is assessed using standard privacy-sensitive datasets like _Adult Income_, _MIMIC-III_, and _credit card fraud transaction records_, etc. To evaluate the performance of the framework, standard methods like AES-based encoding, differential privacy algorithms, and homomorphic encryption are used as the baselines. Further, the effectiveness of the framework is measured through utility accuracy, computational cost, privacy guarantees, and scalability. These assessments showcase its capability to securely encode and protect sensitive information while measuring efficiency in cloud storage systems. The framework supports interactions among multiple users, owners, and service providers, specifically catering to owner-write, user-read scenarios. Through experiments and security analysis, it has been observed that an EKDA efficiently reduces communication and storage overheads, while a DWBF enables encrypted keyword retrieval and further reduces communication, storage, and computation overhead. Figure 4: The privacy-preserving cloud storage framework. The proposed framework notably lowers the risks of data privacy attacks using inference and reconstruction. It applies layered transformations to unclear sensitive attributes and achieves up to an 85% reduction in success rates. Although this method may introduce a processing overhead of around 18% to 25%, it remains efficient and scalable for cloud-IoT-based environments. The proposed design is aligned with data protection standards, e.g., CCPA, GDPR, HIPAA. Further, it promotes anonymization and controlled exposure of data. In a subsequent study conducted by [PERSON] [71], the impact of preserving user secrecy and privacy awareness in the current cloud computing era after five years was carefully examined. With the growing ease of availability and flexibility, the amount of information shared and stored in the cloud environment has significantly increased. However, the sensitive nature of user information in cloud storage makes it vulnerable to attacks from various hackers. Misuse of private information not only compromises user trust but also poses a challenge for cloud providers. Therefore, in today's internet world, effective information management and secure maintenance have become crucial tasks. In today's world, various sectors such as healthcare, finance, services, and government rely on the cloud for storing information. However, handling such tasks globally poses significant challenges, making it imperative to have a new framework that ensures user privacy and uninterrupted services. To address this demand, [PERSON] et al. proposed the Petri-net Privacy Preserving Framework (PPPF) to safeguard user privacy and enhance users' trust in cloud providers. The PPPF model is presented in Figure 5, and the corresponding proposed source-to-contract (S2C) privacy-preserving cohesion technique is presented in Figure 6. The framework collaborates with PPCT (_Privacy-Preserving Contact Tracing_) to promote, develop, validate, and increase the importance of data privacy. The PPPF focuses on securing confidential data in storage areas, ensuring the performance of cloud services, and verifying unknown users' legitimacy.