# Secure Cloud Infrastructure: A Survey on Issues, Current Solutions, and Open Challenges
## 5 Related Existing Solutions in Cloud Levels
### Solutions at Data Level  
Through the transition from conventional computing models to the Internet-based cloud model, there is a great need for emphasizing data security and privacy. Data loss or data leakage can significantly affect the organization's business and ruin the trust in its brand. A study in [49] investigated the audit in the cloud computing environment. Data auditing involves examining various features that include data confidentiality, integrity, remanence, provenance and lineage. According to the study, there are a range of basic techniques in each of these features that could satisfy the needs of cloud service users for data auditing, except data remanence, which is still an open issue within public cloud services. As concluded, the study found that despite many available techniques to address user auditing issues in the data auditing area, cloud providers have so far focused more on infrastructure security auditing than data auditing. The authors in [50] focused on the issue of data integrity verification by a third-party auditor for client data that resides on a cloud storage server (CSS). The study suggested a protocol for dynamic data updates using the modified Chameleon Authentication Tree (MCAT). They also demonstrated the security of their optimized auditing protocol by proving that it is resistant to replay, replace, and forge attacks. A study in [51] proposed a classification technique based on various parameters. The parameters were defined based on different dimensions. It is intended to have security levels based on content type and accessibility. According to the authors, data security can be provided based on the level of protection needed. Depending on the data set classified as dimensions, the corresponding security provisions for storage can be applied. In [52], the authors proposed a secure data classification-based cloud computing model. The proposed model minimizes the total time necessary to secure data by applying TLS, AES and SHA cryptographic algorithms based on the classified data type. The proposed model has been tested and the results show the reliability and efficient existence of the proposed model. In [53], the authors proposed a framework for privacy-preserving out-sourced classification in cloud computing (POCC). Using POCC, the evaluator can train a classification model securely over data encrypted with different public keys, that are outsourced to multiple data providers. Based on [PERSON]'s scheme, the authors used a proxy fully homomorphic encryption technique to protect the privacy of sensitive data. The work in [36] defined the data security modeling design in cloud computing. Data security in all cloud storage layers was discussed. Based on this study, the standard cloud storage uses a three-level cloud data security model that can be expanded to a fourth level responsible for data integrity checks. The paper introduced the design of a four-level data security model in cloud computing that describes each part of cloud data security using Petri nets. The authors in [54] proposed a framework to protect big data in the cloud computing environment. The Map Reduce framework was used to find the number of users logging into the cloud data center. The proposed framework protects the mapping of different data elements to each provider using the meta cloud data storage interface. While this proposed approach requires a high degree of implementation effort, it offers valuable information for a cloud computing environment that can have a high impact on future systems. A study in [55] proposed a framework consisting of various techniques and specialized procedures that can protect the data efficiently from the owner to the cloud and then to the user. Data protection strategies include a secure socket layer (SSL) and MAC, which are used to check the integrity of data, to encode the data, and divide it into three sections of the cloud. The division of data into three sections offers additional protection and more accessibility.