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p-Index From 2021 - 2026
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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Karemallaiah, Jayalakshmi
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Homomorphic technique for group data sharing in cloud computing environment Karemallaiah, Jayalakshmi; Revaiah, Prabha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6612-6618

Abstract

The main aim of this research work is to make it easier for the same group to share and store anonymous data on the cloud securely and effectively. This research work presents verifiable privacy-aware enhanced homomorphic (VPEH) for multiple participants; moreover, the enhanced homomorphic encryption mechanism provides end-to-end encryption and allows the secure computation of data without revealing any data in the cloud. The proposed algorithm uses homomorphic multiplication to compute the hashes product of challenges blocks that make it more efficient, furthermore an additional security model is incorporated to verify the shared data integrity. The VPEH mechanism is evaluated considering parameters such as tag generation, proof generation, and verification; model efficiency is proved by observing the marginal improvisation over the other existing models by varying the number of blocks and number of challenge blocks.
Verifiable data distribution technique for multiple applicants in a cloud computing ecosystem Karemallaiah, Jayalakshmi; Revaiah, Prabha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1241-1249

Abstract

Cloud computing is the most exploited research technology in both industry and academia due to wide application and increases in adoption from global organizations. In cloud, computing data storage is one of the primary resources offered through cloud computing, however, an increase in participants raises major security concerns, as the user has no hold over the data. Furthermore, recent research has shown great potential for efficient data sharing with multiple participants. Existing researches suggest complicated and inefficient cloud security architecture. Hence, this research work proposes identifiable data sharing for multiple users (IDSMU) mechanism, which aims to provide security for multiple users in a particular cloud group. A novel signature scheme is used for identifying the participants, further verification of the Novel Signature Scheme is proposed along with a retraction process where the secret keys of the participant and the sender is cross-verified; at last, a module is designed for the elimination of any malicious participants within the group. IDSMU is evaluated on computation count and efficiency is proved by comparing with an existing model considering computation count. IDSMU performs marginal improvisation over the existing model in comparison with the existing model using the novel signature scheme. 
Privacy-aware enhanced homomorphic mechanism for group data sharing Karemallaiah, Jayalakshmi; Revaiah, Prabha
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1805-1816

Abstract

Cloud-based group data sharing has gained huge popularity in recent years. Accomplishing the efficacy and security of the data in a cloud-computing framework is challenging. Sharing data in a cloud environment is quite challenging and needs to be resolved. Furthermore, while exchanging data on the cloud, it is challenging to achieve both anonymity and traceability. The main aim of this research work is to make it easier for the same group to share and store anonymous data on the cloud securely and effectively. This research work presents verifiable privacy-aware enhanced homomorphic (VPEH) encryption for multiple participants; moreover, the enhanced homomorphic encryption mechanism provides end-to-end encryption and allows the secure computation of data without revealing any data in the cloud. The proposed algorithm uses homomorphic multiplication to compute the hashes product of challenges blocks that makes it more efficient Furthermore, an additional security model is incorporated to verify the shared data integrity. The VPEH mechanism is evaluated considering parameters such as tag generation, proof generation, and verification; model efficiency is proved by observing the marginal improvisation over the other existing model by varying the number of blocks and several challenge blocks.
Co-Authors Revaiah, Prabha