Abstract
With the exponential growth of network technology, data sharing becomes more popular and various scholars have carried out comprehensive researches for promoting its development. Multi-party data sharing generally involves multiple participants to access and share data in a trustless environment. To gain the trust of users, it is significant to provide security guarantees that protect user's privacy and obstruct unauthorized access. Therefore, it is essential to guarantee the integrity, confidentiality and trustability of sensitive data and to overcome the risks associated with malicious attacks and unauthorized access. To bridge this gap, this research introduces Migration Flamingo Search Algorithm (MFSA) for multi-party data sharing in the blockchain using hierarchical homomorphic encryption. Initially, data owner (DO) submits a file to cloud through the smart contract (SC). Thereafter, encryption of data files is done employing Hierarchical Multipart Homomorphic Encryption (HMHE). Here, Hierarchical Squeeze Forward Fractional Net (HiSqFFNet) is employed to generate hierarchical key. HiSqFFNet is introduced by combining of Hierarchical Deep Learning Neural Network (HiDeNN) with SqueezeNet. After encryption, an encrypted data is stored in Interplanetary File Systems (IPFS). Then, access request is sent by a data requester (DR) for accessing an encrypted data. The trust manager evaluates trustability of DR and the process is accomplished using MFSA that is presented by incorporating Migration Algorithm (MA) with Flamingo Search Algorithm (FSA). If the trust is acceptable, data from IPFS is decrypted and then, file is delivered to DR. The proposed MFSA is assessed with different evaluation metrics and the evaluation is done using heart disease dataset. Additionally, MFSA has obtained minimal decryption time of 10.548sec, encryption time of 7.978sec, key complexity of 0.727, time complexity of 0.455sec for 100 kb data size with key size = 64.
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