Improved Signcryption algorithm for cloud virtual machines

Yogesh Gurav

doi:10.1016/j.compeleceng.2025.110499

Improved Signcryption algorithm for cloud virtual machines

2025-07-12

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Sathish Kumar Selvaperumal

S

S. D. Shirbahadurkar

DOI：10.1016/j.compeleceng.2025.110499

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Over the past few years, cloud computing has grown in popularity across all corporate sectors worldwide. However, security continues to be a major roadblock in the dependability of the cloud computing platform. As a result, tenant data security isolation is crucial for protecting sensitive tenant data. This paper's main goal is to present a new decentralized information flow control system for cloud virtual machines with multiple tenants. Three main elements make up the cloud computing environment: (a) Cloud Service Provider (CSP), (b) Data Store (DS), and (c) User. The Cloud Service Provider (CSP) is embedded with the newly proposed decentralized information flow control, and it enables the flow of control of data policies for regulating the flow of data between the cloud users within the cloud. The proposed technique involves three primary stages (i) data identification (ii) trust evaluation (iii) optimal key-based cipher-text information flow security. Initially, according to the security values, tactful information is derived from the input data, which is secured in the CA of CSP to safeguard the labels and keys authentically, subsequently, the decryption process takes place. Then the EP exhibit the flow of ciphertext data based on the proposed estimation of trust. The proposed improved Signcryption algorithm is employed to cipher the user's information and the optimal key is generated by using the proposed improved Sun Flow Optimization Algorithm, which ensures that data remains confidential and tamper-proof. Ultimately, based on the proposed trust value, a decryption key will be offered to users like direct users and indirect users, in which the direct users are the old users already having access rights whereas the indirect users are the new users. The proposed model is examined over various existing techniques, in which the cost function of the proposed work for the lung cancer dataset is 14.2%, 15.7%, 13.6%, 12.1%, 11.5% and 15.2% greater than the current methods like WOA, LA, SSA, ABC-GOA, FSO and SFO respectively. Therefore, the proposed ISFO has been proven to be very concurrent with the traditional optimization approaches, and this aids in increasing the security of the data.

Improved Signcryption algorithm for cloud virtual machines

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