DACAS: Dual Attentive Consensus Mechanism‐Enabled Authentication Security Scheme for Secure Healthcare Data Storage
The efficient and secure management of healthcare records is crucial, as it contains sensitive medical information. Any modifications or tampering in the medical information can result in dreadful conditions due to the wrong diagnosis. Existing methods deployed for maintaining secure storage faced complexities due to larger key lengths, resource requirements, and interoperability issues across a broader deployment. Therefore, to address these limitations, the Dual Attentive Consensus mechanism‐enabled Authentication Security (DACAS) scheme is proposed in this research. Specifically, the Dual Attentive Consensus mechanism (DAC) handles the block creations and blockchain transactions to elevate the security against malicious validator nodes and balances the efficiency of the system. Moreover, the proposed method utilizes the dual verification mechanism that involves the Advanced Encryption Standard (AES) algorithm for encrypting the user credentials and the Elliptic Curve Digital Signature Algorithm (ECDSA) for signature generation, bolstering data integrity and authenticity. Additionally, the Ring Learning with Errors (RLWE) Homomorphic algorithm is employed to encode the patient data before storing it in the cloud via blockchain. The method generates the ciphertext without altering the original contents and improves the data integrity by providing strict privacy compliance. Extensive experiments demonstrate that the proposed DACAS scheme exhibits higher security, outperforming the existing BDSS and AES‐IDEA methods with a time delay of 1.48 ms, GUR of 0.902, gas usage of 13414.78, and verification time of 2.54 ms for evaluation over a data volume of 100 KB/patient and network size of 250 nodes. Further, the experimental results show that the DACAS scheme significantly lowers the processing cost of both encryption and decryption, while elevating the security level of data storage.