Fast Syndrome-Cryptographic Hash Storage Based Tanimoto Index Margin Relaxing Support Vector Regressive Data Auditing With Iot

Data auditing in the cloud server has more significance than any other data protection mechanism to ensure the integrity of the user data. When users store their data, integrity is a major concern for data owners due to the lack of direct control. However, the existing remote data auditing schemes f...

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Bibliographic Details
Published inWebology Vol. 18; no. 5; pp. 1 - 14
Main Authors Sivakamasundari, S, Dharmarajan, K
Format Journal Article
LanguageEnglish
Published Tehran Dr. Alireza Noruzi, University of Tehran, Department of Library and Information Science 01.01.2021
Subjects
Auditing
Audits
Big Data
Blockchain
Cloud computing
Data integrity
Performance evaluation
Privacy
Registration
User behavior
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Summary:Data auditing in the cloud server has more significance than any other data protection mechanism to ensure the integrity of the user data. When users store their data, integrity is a major concern for data owners due to the lack of direct control. However, the existing remote data auditing schemes for big data platforms are difficult to provide a higher integrity rate. In order to improve the integrity verification of data on cloud storage, A Fast Syndrome-Cryptographic Hash Storage based Tanimoto index Margin Relaxing Support Vector Regressive Data Auditing (FSCHS-TIMRSVRDA) technique is introduced. The FSCHS-TIMRSVRDA technique has three steps, namely the registration phase, generation phase, and verification phase. In the registration phase, the user registers his detail to a cloud server for storing their data collected from IoT. IoT collects the information from an entity and sends the information to the cloud server. In the generation phase, the FSCHS-TIMRSVRDA technique uses the Fast Syndrome-Cryptographic Hash function to generate the hash value for the cloud user data and send it to the cloud server for dynamic storage. Whenever the cloud user needs to audit the data, the audit request is sent to the third-party auditor (TPA). The TPA receives the audit request and the data from the cloud server for data auditing. TPA generates the hash value for user data. Finally, TPA verifies the generated hash value of data from CS with the hash value of data stored in TPA by using Tanimoto index Margin Infused Relaxing Support Vector Regression. In this way, data auditing is performed in a cloud environment. Experimental evaluation is carried out on factors such as space complexity, data integrity, and data auditing time with respect to a number of cloud user data. Results show that the proposed FSCHS-TIMRSVRDA technique can efficiently enhance the data integrity rate and reduce the space complexity as well as data auditing time.
ISSN:1735-188X