Security enhancement of decentralized healthcare system by transformer blockchain mechanism - Goel, Akanksha; Neduncheliyan, Subbu (Engineering and Applied Science Research, 2024)
Medical data plays an essential role in diagnosing diseases and planning therapeutic. However, securing these data is a very critical function in the healthcare system. Some of the traditional Encryption and decryption mechanisms have resulted in a loss of sensitive medical information. In addition, maintaining the confidential score of the medical information is a much more needed and essential task. Considering these cases, the healthcare application was adopted in this present study. Therefore, to enhance security, a novel Transformer Neural Data Encryption Blockchain (TNDEB) has been proposed in this research. The IoMT database was initially collected and trained to detect and eliminate malicious events. Further, the hashing and encryption process has been carried out to secure the data. Moreover, to check the data similarity, the homomorphism function was performed at the verification module, and the verified data was decrypted using the shared private key. The chief contribution of this study is to keep medical information confidential with the support of the holomorphic concept. Additionally, the cryptanalysis was carried out by launching the brute force attack to compute the performance efficiency of the TNDEB model. Subsequently, the validated performance results are compared with existing models. The decrypt and encrypt time achieved by the TNDEB model is 1.260ms and 1.010ms, respectively. In addition, the gained confidential score is 98.8%. Hence, the proposed model is highly suitable for the IMoT application to secure the information at a high confidential rate.