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TAR-AFT: A Framework to Secure Shared Cloud Data with Group Management

K. Ambika1,*, M. Balasingh Moses2

1 Department of Computer Science Engineering, University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, India
2 Department of Electrical and Electronics Engineering, University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, India

* Corresponding Author: K. Ambika. Email: email

Intelligent Automation & Soft Computing 2022, 31(3), 1809-1823.


In addition to replacing desktop-based methods, cloud computing is playing a significant role in several areas of data management. The health care industry, where so much data is needed to be handled correctly, is another arena in which artificial intelligence has a big role to play. The upshot of this innovation led to the creation of multiple healthcare clouds. The challenge of data privacy and confidentiality is the same for different clouds. Many existing works has provided security framework to ensure the security of data in clouds but still the drawback on revocation, resisting collusion attack along with privacy of data present a complex problem. For preserving the data privacy and confidentiality, a novel framework is proposed with two novel algorithms of Threat Aware Revocation (TAR) and Advance Flexi Twister Secret Block Encryption Standard (AFT-SBES) both are named as TAR-AFT. The TAR algorithm is mainly focus on generates the user signature and the AFT-SBES algorithm to generate the key. Both the signature and key are distributed to the users for enhancing the security to access cloud storage and files. The self-session shadow approach is used to monitor the activities of the cloud users. The revocation is carried out through removal of signature from the Enhanced Dynamic Hash Table (EDHT). From the performance analysis of TAR-AFT, it provides more effective to accessing of data stored in cloud and security such as data privacy and confidentiality.


Cite This Article

K. Ambika and M. Balasingh Moses, "Tar-aft: a framework to secure shared cloud data with group management," Intelligent Automation & Soft Computing, vol. 31, no.3, pp. 1809–1823, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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