Design and Implementation of a Data Encryption Framework Simulink Model for Protecting Sensitive Organizational Data

Internet-of-Medical-Things (IoMT) based sensitive data protection has become a critical problem lately. IoMT-sensitive data are used to connect patients with adults. Since every record, data, and healthcare signal is transmitted over these systems, they need strong and safe access methods for individuals to transmit their essential statistics or documents. Because of this, breaching these systems could have negative impacts on patients. An emerging pattern in biometric verification is to prevent using authentic biometrics for security purposes. Cancelable digital fingerprints can be created using a data encryption framework or non-invertible transformations. This study presents an integrated structure for cancelable ECG signal detection that is suitable for use in IoMT networking access steps. The suggested system uses fuzzy reasoning to perform a non-invertible transition on ECG data, altering its shifting spectrum. Cancellable biometric platforms aim to retrieve original ECG data from processing variants, but the procedure is non-invertible and hinders this goal. Next, customized trends are used to build compact encryption using XOR. This eliminates the complication of comprehensive encryption systems requiring significant computing resources. By combining encryption techniques and non-invertible transformations, the encryption phase improves the safety of cancelable biometrics features and permits the mixed nature of the suggested cancelable biometrics architecture. For the actual adoption of the suggested ECG-based cancelable biometrics identification structure, hardware adaptation is also shown. According to empirical findings, the suggested structure performs well, with an Equivalent Error Rate (EER) of 0.059%.