Data Security and Privacy Protection for Cloud Storage: A Survey

Abstract

The new development trends including Internet of Things (IoT), smart city, enterprises digital transformation and world's digital economy are at the top of the tide. The continuous growth of data storage pressure drives the rapid development of the entire storage market on account of massive data generated. By providing data storage and management, cloud storage system becomes an indispensable part of the new era. Currently, the governments, enterprises and individual users are actively migrating their data to the cloud. Such a huge amount of data can create magnanimous wealth. However, this increases the possible risk, for instance, unauthorized access, data leakage, sensitive information disclosure and privacy disclosure. Although there are some studies on data security and privacy protection, there is still a lack of systematic surveys on the subject in cloud storage system. In this paper, we make a comprehensive review of the literatures on data security and privacy issues, data encryption technology, and applicable countermeasures in cloud storage system. Specifically, we first make an overview of cloud storage, including definition, classification, architecture and applications. Secondly, we give a detailed analysis on challenges and requirements of data security and privacy protection in cloud storage system. Thirdly, data encryption technologies and protection methods are summarized. Finally, we discuss several open research topics of data security for cloud storage.

Towards a Modular On-Premise Approach for Data Sharing

“…This solution helps to achieve secure storage of information at a reduced price of the cloud providers, maintaining the data encrypted outside of the perimeter of the infrastructure to allow sharing with other entities to maintain user privacy. In addition, it can help to solve some open research challenges, such as designing secure privacy protection schemes to ensure that sensitive information in shared data is hidden, and designing efficient and secure third-party privacy protection schemes to support more machine learning algorithms [ 1 , 5 ].…”

“…The issue with re-identification—the process of identifying an individual using the collected data—is even greater in cases where data collected by different companies are aggregated, creating a larger dataset and increasing the likelihood of re-identification [ 5 ].…”

Forecast and Analysis of Coal Traffic in Daqin Railway Based on the SARIMA-Markov Model

“…R 2 is the goodness of fit of the entire model; the higher the R 2 value, the better the fit degree, where R 2 ∈ [0, 1]. AIC and SC are information criteria; the lower the value, the better the fitting degree of the model [33]. e test results of five compared models are provided in Table 2.…”

Privacy-Preserving Image Classification With Deep Learning and Double Random Phase Encoding

“…Security is a significant concern for data generated by IoT devices or data stored in the cloud because there is a high probability that some personal information would be included in this digital information [6][7][8][9][10]. Therefore, many privacy-preservation approaches, such as data encryption, have been proposed to make data transmission, storage, and extraction safe [11][12][13][14]. On the other hand, data encryption in the cloud can decrease the data processing efficiency for some applications, such as image classification and retrieval, because decrypting and then classifying or querying a large number of encrypted images may necessitate considerable computation resources.…”