An Adaptive Methodology for Integrity Checking in Cloud Storage
L Jambulingam1, T V Ananthan2, P S Rajakumar3
1L Jambulingam*, Research Scholar, Department of Computer Science and Engineering, Dr. M.G.R. Educational and Research Institute University, Chennai, India.
2T V Ananthan, Professor, Department of Computer Science and Engineering, Dr. M.G.R. Educational and Research Institute University, Chennai, India.
3P S Rajakumar, Professor, Department of Computer Science and Engineering, Dr. M.G.R. Educational and Research Institute University, Chennai, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 4470-4475 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8989088619/2019©BEIESP | DOI: 10.35940/ijeat.F8989.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Majority of the organization uses cloud for storage purpose in order to reduce the cost as well as maintenance. Due to increasing threat from internal and external sources, there would be possibility of corruption in the cloud storage files. Thus the storage must to be monitored periodically for integrity checking. Since most of the Data Owners have limited resources thus the responsibility of integrity checking goes to the Third Party Auditors (TPA). Usually the static way of deciding to use particular hash tree methodology to store the cloud storage meta-data, which is mainly used for integrity checking throughout is inappropriate for two main reasons, firstly, due to more fluctuated loss or corrupted cloud data, secondly, based on the variations in the number of files in the cloud users’ directory; Initially the static approach would be good but it may not be optimal solution at the later period. therefore, in this paper, we have proposed Adaptive Integrity Checking method (AIC), which would lead a way for adaptive dynamic hash tree methodology for holding the cloud storage meta-data; which would drastically increases the performance of integrity checking in terms of both time and space complexity besides the benefits obtained in the EDHT-n version and HEDHT methodologies of handling the cloud storage integrity checking.
Keywords: Third Party Auditor (TPA), Adaptive Integrity Checking (AIC), Hybrid Enhanced Dynamic Hash Tree (HEDHT), Enhanced Dynamic Hash Tree (EDHT), Meta-Data, Cloud Service Provider (CSP), Microservice, API.