The demand for cloud computing has increased immensely, and its security is becoming challenging. The enormous growth in cloud computing adaptation has been observed, but the information security concerns have not been addressed thoroughly. The security issues related to cloud computing are a concern. The emergence of Blockchain as a key security provider has increased the hope for the availability of a secure cloud computing environment. The data-sharing technique based on the cloud scenario relies on the network's storage and architecture; however, the storage providers are considered trusted third parties for data-sharing and storage purposes. The associated limitations such as security, high operational cost, centralized storage capability, and data availability have become a challenging task, which leads to the development of a trusted data management system for secure data sharing through the Blockchain. This study presents an analysis of secure data-sharing techniques using Blockchain. The related research articles were elicited from several sources such as Springer, IEEE, Elsevier, and other online sources. The primary studies have been categorized into four types: healthcare data sharing, vehicular communication-based data sharing, IoT-based data sharing, and other miscellaneous techniques. The techniques have been analyzed based on various performance metrics. The analysis and findings of this study can pave a way for the future development of safe data-sharing techniques using Blockchain technology.

In today’s era, ensuring the security and confidentiality of data sharing over blockchain network is a significant challenge. Various algorithms and security schemes have been introduced but, persistent threats remain due to weak access control policies, compromised keys, and vulnerable encryption methods. To address these issues, this study proposes a Multikey-enabled Montgomery Curve (MkMgC) scheme for confidential and secure data sharing over the blockchain system. As the name indicates, the proposed MkMgC scheme includes a multikey concept and keys are obtained over the Montgomery curve that allows fast computation with high resistance to side-channel attacks, particularly securing data while transaction. The proposed MkMgC scheme is applied to both authentication and encryption processes. It incorporates the Multikey Montgomery Curve Digital Signature Algorithm (M2CDSA) for authentication and the Multikey Montgomery Curve Cryptography (M2CC) for encryption. These integrated algorithms significantly enhance security while maintaining computational efficiency, allowing for timely data transmission within blockchain networks. Due to its effectiveness, the MkMgC scheme achieves superior performance across several metrics: encryption time (1.98 s), false user rate (0.18), gas transaction (364.92 KB), genuine user rate (0.82), memory usage (390.70 KB), responsiveness (8.45 s), and transaction time (6.01 s), outperforming existing solutions.

Myocardial infarction (MI) remains one of the leading causes of cardiac-related mortality globally, posing a significant burden on public health systems, including in India. Early and accurate diagnosis is crucial for the effective management and treatment of MI and its various subtypes. Electrocardiogram (ECG) serves as a primary, non-invasive diagnostic tool due to its capacity to reveal critical cardiac anomalies, particularly through analysis of the QRS complex. However, reliable interpretation of ECG signals is challenged by artifacts such as baseline wander, motion interference, and muscle contraction noise. The study explores about spectral techniques such as Stationary Wavelet Transform (SWT) to decompose ECG signals into relevant timefrequency domains. From these decomposed spectral bands, nonlinear entropy-based features are extracted to capture the inherent complexity and irregularities within the MI signal dynamics. These features are then employed to train multiple machine learning classifiers including Random Forest (RF), Bayesian Optimized Support Vector Machine (BOSVM), Adaptive K-Nearest Neighbours (AKNN), SVM (Support Vector Machine), AdaBoost, Decision Tree (DT), and Extreme Learning Machine (ELM). The simulation results indicated that the combined SWT and Sample Entropy and Bubble Entropy approach achieved an accuracy of 99.51%, specificity of 99.63%, sensitivity of 98.72%, precision of 98.6%, and F1-score of 98.59%.