Privacy-preserving public auditing for data integrity in cloud

Saleem, Mohd; Murali, M.

doi:10.1088/1742-6596/1000/1/012164

Cited by 5 publications

(1 citation statement)

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“…Lee KM et al [21] introduced a novel system for verifying the integrity of remotely stored data. M. Shaik Saleem and M. Murali [22] proposed a method for conducting public audits of data integrity in the cloud while preserving privacy. Y. Yannan Li Y., Yong Yu et al [23] proposed a data integrity auditing method based on fuzzy identities aimed at enhancing reliability in cloud storage systems.…”

Section: Related Workmentioning

confidence: 99%

Optimal Method Combining Blockchain and Multi-Agent System to Ensure Data Integrity and Deduplication in the Cloud Environment

El Ghazouani,

Ikidid,

Ait Zaouiat

et al. 2024

Int. J. Interact. Mob. Technol.

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Cloud computing (CC) refers to the transmission, storage, and processing of any type of information at a location that is not owned or controlled by the information owner. This information can be stored and accessed anytime, from anywhere, and using any device. However, several challenges threaten the deployment of cloud computing technology within organizations, including user privacy, data integrity, and managing the retention of the vast amount of data that will be stored and analyzed in this environment. In recent years, various data integrity checking systems and data storage mechanisms have been proposed to address these diverse challenges. Nevertheless, some protocols do not guarantee data confidentiality against auditors during the auditing process, while others do not provide batch auditing. Additionally, the majority of these protocols do not support the deduplication technique. Therefore, in this research work, the security features of blockchain technology and those of multi-agent systems (MAS) have prompted us to propose models that enable both data deduplication, saving storage space, and data integrity auditing, efficiently verifying the accuracy of data outsourced to CC environments. Implementation findings indicate that the suggested system is highly feasible and applicable in real-world scenarios.

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Section: Related Workmentioning

confidence: 99%