Abstract
With the growing relevance of data integrity, public auditing mechanisms for cloud storage models are intensively researched. In public auditing systems, a third person is used to ensure the outsourced data’s integrity on the user’s behalf. A lot of public verification models regarding blockchain are developed to combat cruel trusted Third Public Auditors (TPAs). Conventional models for auditing are based on TPA and are prone to an auditor enticing who colludes with the hostile block-chain to provide prejudiced results for auditing. This research work plans to propose a blockchain-based public auditing scheme to verify data integrity in cloud storage. The first people upload the data to the cloud and retrieve the data when they need it. The data must be encrypted and private among the second people and DO, since both DO and the second person do not believe each other. Therefore, the DO encrypts his/her data using the Improved EI-GAMAL (IEI-GAMAL) encryption algorithm and uploads the encrypted data to the Cloud Service Provider (CSP) via the blocks of the blockchain. For preserving the privacy of the data, the key for data encryption as well as decryption is generated using the new hybrid optimization model. The DO selects one TPA to audit the data on behalf of the DO. Generally, a TPA concentrates the data of numerous DOs and offers the audit for DOs. The DO requests a public audition to generate proof of the encrypted block stored in the blockchain when he requires verifying the data present in cloud storage. In order to provide strong updates and lessen the computation expenses, the new hybrid optimization algorithm is used. The new hybrid algorithm is the theoretical unify of the Adaptive Position-based Arithmetic with the Sunflower Optimization Algorithm (APA-SFOA). Several investigations are executed to prove the supremacy of the developed model.
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Kumar, R.P., Bandanadam, S.R. Block chain-based decentralized public auditing for cloud storage with improved EIGAMAL encryption model. Int. j. inf. tecnol. 16, 697–711 (2024). https://doi.org/10.1007/s41870-023-01599-8
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DOI: https://doi.org/10.1007/s41870-023-01599-8