Adaptive Deep Conditional Random Field-Based Blockchain Access with Hybrid Encryption for Data Privacy Preservation

Data Privacy Preservation (DPP) is a control measures to protect users sensitive information from third party. The DPP guarantees that the information of the user’s data is not being misused. User authorization is highly performed by blockchain technology that provide authentication for authorized user to utilize the encrypted data. Effective encryption techniques are emerged by employing ̣ deep-learning network and also it is difficult for illegal consumers to access sensitive information. Traditional networks for DPP mainly focus on privacy and show less consideration for data security that is susceptible to data breaches. It is also necessary to protect the data from illegal access. In order to alleviate these issues, a deep learning methods along with blockchain technology. So, this paper aims to develop a DPP framework in blockchain using deep learning. Hence, the Adaptive Deep Conditional Random Field (ADCRF) is a deep learning network utilized for accessing the blockchain in a secure manner. The parameters in the ADCRF network are optimized by Improved Running City Game Optimizer (RCGO). For securely accessing the blockchain, the data is stored after the encryption process. The data privacy is ensured by the Hybrid Elgammal-Attribute-based Encryption (HEA) method. Hybrid encryption methods enhance the security during the data preservation process. The numerical findings of the developed model are differentiated from the existing baseline works to ensure the performance of the proposed approach. The experimental findings of the developed model attain 96.35, 96.27, 96.40, 93.48 and 94.85 regarding accuracy, recall, specificity, precision, and F1-score respectively. This analysis could strengthen the developed framework to secure data with the help of user authentication model.