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28-07-2021

Secure data sharing using Merkle hash digest based blockchain identity management

Journal:
Peer-to-Peer Networking and Applications
Authors:
Tripti Rathee, Parvinder Singh
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Abstract

Blockchain identity management (BIdM) is a mechanism for identification, authentication and approval of user right to use the personal data. In the recently established BIdM systems, the third party takes control of maintaining the confidential data, and they are responsible for the whole activity. Since the third party must be a trusted one for managing the whole transaction activity, therefore identifying the trusted third party is the major challenge in the BIdM. Also, the data sharing through centralized system is a complex task due to the presence of attackers. To address these issues, a Merkle hash digest based BIdM approach is proposed in this paper to share the data securely.The proposed approach involves three phases namely- protection of authentication, ownership protection and identity mapping validation. In the first phase, the public key and private key are generated using the modified Elliptic curve cryptography (MECC). After the key generation, the data is encrypted using the Merkle hash digest algorithm (MHDA) for ensuring the personal user data security. After the encryption process, the hash value is evaluated by MD5. In the second phase, the score for every data in the block is generated using enhanced Message Content Recommendation Algorithm (EMCRA) and Latent Dirichlet Allocation algorithm (LDA). The data with the highest score value is sent to the user for the authentication purpose. In the final phase, the matching hamming criteria are used for the identity validation; here, the hamming distance is evaluated for comparing the strings between two hash function. JAVA is used to implement the proposed method and in experimental outcomes, an analysis is performed on communication delay, communication overhead, execution time, encryption time and key generation time. Based on communication delay, the proposed method with LDA consumes 0.01027 ms for 20 keywords, communication overhead for 10 keywords are 10 bytes of data and the total time consumed by the proposed approach for the computation is 34.52 ms. From the analysis, the overall performance of the proposed method showed a better result when compared to the existing techniques.

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