# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 4 The Anticipated Cloud Storage Structure
### Contributions and Improvements in Existing Ideas
These storage areas act as safeguards for preserving highly sensitive information. The main components of the Petri-net Privacy Preserving Framework system, as illustrated in Figure 7, include cloud service request, user request verification, cloud requestors authorization, user validation, and cloud users authentication or response. Figure 5: Petri-net privacy-preserving workflow in the cloud. Storage-as-a-service (STaaS) is a prominent privacy-preserving technology used in cloud computing, enabling the distributed storage of massive amounts of remote data. The advent of _mass-distributed storage_ (_MDS_) has further expanded storage capacity. However, the arrangement of distributed storage can expose vulnerabilities to malicious attacks,  
Figure 6: S2C privacy preserving cohesion technique. Figure 7: Architecture of the proposed SA-EDS model. particularly during data transmission. [PERSON] et al. introduced the _Security-Aware Efficient Distributed Storage (SA-EDS)_ model in 2017 as an intelligent cryptographic approach to enhance privacy preservation [72]. The SA-EDS model utilizes information science to develop an efficient MDS service with robust security measures. Data encryption is employed prior to distributive storage across multiple cloud servers, ensuring minimal overhead and latency. The architecture of SA-EDS is depicted in Figure 7.  
To enable data searching ability for users, named-data-packet techniques are utilized, represented by solid (S) and broken (B) arrow lines. B arrow lines indicate data retrieval operations, while S arrow lines denote data splitting and storage operations. Additionally, the _Secure Efficient Data Distributions (SED2)_ algorithm ensures secure data splitting with minimal cost to prevent information leakage. The decryption process for sensitive data retrieval is supported by electronic data capture approaches. In 2018, [PERSON] et al. [73] proposed the privacy-protecting _Principal Component Analysis_ (_PCA_) framework for ensuring privacy protection to predict data and results. By keeping the key for decrypting the encrypted content in the hands of the owner instead of the cloud machine, the framework ensures that even if the cloud provider experiences a data breach, only the encrypted information is leaked, preventing the exposure of actual records. The services provided by cloud security systems are described in Figure 8.  
The approach mentioned in Figure 8 involves protecting data based on its content and applying privacy protection based on the assigned importance level. This determination is made by analyzing the content of transmitted data from each individual and aligning it with the requirements defined in the \"policy.\" This method enables the secure and simultaneous utilization of services that involve personal data while preserving privacy. In  
Figure 8: Cloud security services. 2019, [PERSON] introduced the Data Bank, a cloud-IoT architecture designed to protect users' sensitive data [74]. The architecture allows users to control the transmission of data from their devices and provides tools for visualizing agreements and managing privacy-utility trade-offs. [PERSON] illustrates their work with an example in the context of a smart vehicle environment. The proposed 4-tier architecture for cloud-IoT platforms is depicted in Figure 9. It includes the application layer, cloud layer, data pocket layer, and sensors layer (physical objects) [74]. Detailed descriptions of each layer are provided by [PERSON]. In 2022, [PERSON] et al. [75] introduced a novel privacy-preserving cloud computing solution known as _Multi-Class Privacy-Preserving Cloud Computing_ (_MPCC_). This groundbreaking scheme utilized compressive sensing techniques to represent sensor data in a compact manner, ensuring efficient data encryption. The MPCC framework comprises three distinct variants, each tailored to address specific use cases: statistical decryption for smart meters, data anonymization for images, and electrocardiogram signal processing.