Data integrity for dynamic big data in cloud storage: a comprehensive review and critical issues

Abstract

Cloud storage services provide vast storage space to solve the bottleneck of the data generated by different big data applications. However, the nature of big data in terms of its massive volume and rapid velocity, needs to be considered when designing data integrity schemes to provide security assurance for data stored in the cloud. The state of the art of data integrity in the cloud includes two primary schemes: (i) Proof of Retrievability (POR) and (ii) Provable Data Possession. Both techniques are designed to achieve the same goal in ensuring data integrity of outsourced data in cloud storage; However, PoR varies from PDP by error-correcting feature to retrieve the damaged outsourced data. This paper focuses on the proof of data retrievability technique (POR) for dynamic data. Dynamic data is defined as data under different update operations. The paper surveys the state of the art data integrity techniques for cloud storage (CS) and previous work on basic requirements for an effective data integrity technique for big data applications. Methods used to provide dynamic PoR are discussed before summarizing the classification of the POR state-of-the-art. The recently proposed techniques and their limitations are also discussed with issues to consider for future POR scheme design.