Volume 16 , Issue 1 , PP: 134-119, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
A. Jeneba Mary 1 * , K. Kuppusamy 2 , A. Senthilrajan 3 *
Doi: https://doi.org/10.54216/JCIM.160111
As an increasing number of people and corporations move their data to the cloud side, how to ensure efficient and secure access to data stored on the cloud side has become a key focus of current research. Attribute-Based Encryption (ABE) is largely recognized as the best access control method for safeguarding the cloud storage environment, and numerous solutions based on ABE have been developed successively. Attribute-based encryption (ABE), which provides fine-grained access control and ensures data confidentiality, is widely used in data sharing. Hence, the strong and lightweight encryption schemes need more limelight of implementation in ABE to overcome the tampering and leakage problem that may cause the severe consequences to the users. To solve this problem, this paper proposes the Swarm Inspired Chaotic Encryption principles for designing the CP-ABE Systems for effective data sharing process. This scheme utilizes the chaotic properties along with the swarm properties for every individual transmission that leads to the strong defence characteristics. The intensive experimentation is carried out using Multi-modal Inputs such as the biometric images and eye iris images. The extensive experimentation is carried out using the various standard tests such as NIST (National Institute of Standard and technology), communication cost (CC) and metrics such as NPCR, UACI, entropies has been evaluated and analysed. Furthermore, excellence of the proposed model is determined by comparing with the other existing schemes. The evaluation demonstrates the CC of proposed scheme is only 30% than other algorithms and passed all the 12 standard tests. The experimental results illustrate the proposed scheme has more advantage in exhibiting the more randomness and light weight characteristics for health care which can more defensive against the attacks
Attribute based encryption , Chaotic Maps , Swarm Intelligence , Biometric Images , Multi-level Inputs
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