Impact of Hybrid LSTM Based Deep Learning Model Over Blockchain Security and Performance Enhancement
Keywords:
Deep Learning, Block chain, Security, PrivacyAbstract
There is a need to maintain patient records and identity management in real time system due to the increasing need for record management. The function of blockchain technology in ensuring the safety of data is the focus of the current study. The main goal of this study is to suggest improvements that use hashing and encryption methods. In this approach, a record is seen as a transaction, and each transaction on a blockchain is recorded and kept in a public ledger as a little piece of labor. Encrypting data before storing it on blockchain is an extra security measure that is now the subject of investigation. To further ensure the safety and efficiency of the blockchain, the SHA-256 hashing algorithm has been used. To further ensure the privacy, authenticity, and immutability of data kept in the distributed ledger, hashing and encryption are essential components of blockchain technology. To prevent unauthorized parties from reading sensitive data, encryption transforms it into ciphertext, which can only be decrypted with the correct cryptographic key. Blockchain security is built upon hashing and encryption, which together provide a strong barrier against data modification, unauthorized access, and guarantee the distributed ledger's reliability. Improvements in security and performance are shown by evaluation of mistake rate, performance, accuracy, and security. We have measured performance in terms of time, security in terms of displaying blocks that have been attacked from the outside, and accuracy in terms of recall, precision, and f1-score.
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