In the domain of Electronic Medical Records (EMRs), emerging technologies are crucial to addressing longstanding concerns surrounding transaction security and patient privacy. This paper explores the integration of smart contracts and blockchain technology as a robust framework for securing sensitive healthcare data. By leveraging the decentralized and immutable nature of blockchain, the proposed approach ensures transparency, integrity, and traceability of EMR transactions, effectively mitigating risks of unauthorized access and data tampering. Smart contracts further enhance this framework by enabling the automation and enforcement of secure transactions, eliminating reliance on intermediaries and reducing the potential for human error. This integration marks a paradigm shift in management and exchange of healthcare information, fostering a secure and privacy-preserving ecosystem for all stakeholders. The research also evaluates the practical implementation of blockchain and smart contracts within healthcare systems, examining their real-world effectiveness in enhancing transactional security, safeguarding patient privacy, and maintaining data integrity. Findings from the study contribute valuable insights to the growing body of work on digital healthcare innovation, underscoring the potential of these technologies to transform EMR systems with high accuracy and precision. As global healthcare systems continue to face the challenge of protecting sensitive patient data, the proposed framework offers a forward-looking, scalable, and effective solution aligned with the evolving digital healthcare landscape.

Due to the overwhelming characteristics of the Internet of Things (IoT) and its adoption in approximately every aspect of our lives, the concept of individual devices’ privacy has gained prominent attention from both customers, i.e., people, and industries as wearable devices collect sensitive information about patients (both admitted and outdoor) in smart healthcare infrastructures. In addition to privacy, outliers or noise are among the crucial issues, which are directly correlated with IoT infrastructures, as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing, i.e., transmitting. Therefore, the development of privacy-preserving information fusion techniques is highly encouraged, especially those designed for smart IoT-enabled domains. In this paper, we are going to present an effective hybrid approach that can refine raw data values captured by the respective member device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT infrastructures. Sliding window, i.e., δ_i based dynamic programming methodology, is implemented at the device level to ensure precise and accurate detection of outliers or noisy data, and refine it prior to activation of the respective transmission activity. Additionally, an appropriate privacy budget has been selected, which is enough to ensure the privacy of every individual module, i.e., a wearable device such as a smartwatch attached to the patient’s body. In contrast, the end module, i.e., the server in this case, can extract important information with approximately the maximum level of accuracy. Moreover, refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the IoT. The proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information perspectives. Simulation and analytical results have proved that the proposed privacy-preserving information fusion technique for wearable devices is an ideal solution for resource-constrained infrastructures such as IoT and the Internet of Medical Things, where both device privacy and information integrity are important. Finally, the proposed hybrid approach is proven against well-known intruder attacks, especially those related to the privacy of the respective device in IoT infrastructures.

The Internet of Things (IoT) and edge-assisted networking infrastructures are capable of bringing data processing and accessibility services locally at the respective edge rather than at a centralized module. These infrastructures are very effective in providing a fast response to the respective queries of the requesting modules, but their distributed nature has introduced other problems such as security and privacy. To address these problems, various security-assisted communication mechanisms have been developed to safeguard every active module, i.e., devices and edges, from every possible vulnerability in the IoT. However, these methodologies have neglected one of the critical issues, which is the prediction of fraudulent devices, i.e., adversaries, preferably as early as possible in the IoT. In this paper, a hybrid communication mechanism is presented where the Hidden Markov Model (HMM) predicts the legitimacy of the requesting device (both source and destination), and the Advanced Encryption Standard (AES) safeguards the reliability of the transmitted data over a shared communication medium, preferably through a secret shared key, i.e., $\lambda$, and timestamp information. A device becomes trusted if it has passed both evaluation levels, i.e., HMM and message decryption, within a stipulated time interval. The proposed hybrid, along with existing state-of-the-art approaches, has been simulated in the realistic environment of the IoT to verify the security measures. These evaluations were carried out in the presence of intruders capable of launching various attacks simultaneously, such as man-in-the-middle, device impersonations, and masquerading attacks. Moreover, the proposed approach has been proven to be more effective than existing state-of-the-art approaches due to its exceptional performance in communication, processing, and storage overheads, i.e., 13%, 19%, and 16%, respectively. Finally, the proposed hybrid approach is pruned against well-known security attacks in the IoT.

The Internet of Things (IoT) is a smart infrastructure where devices share captured data with the respective server or edge modules. However, secure and reliable communication is among the challenging tasks in these networks, as shared channels are used to transmit packets. In this paper, a decision tree is integrated with other metrics to form a secure distributed communication strategy for IoT. Initially, every device works collaboratively to form a distributed network. In this model, if a device is deployed outside the coverage area of the nearest server, it communicates indirectly through the neighboring devices. For this purpose, every device collects data from the respective neighboring devices, such as hop count, average packet transmission delay, criticality factor, link reliability, and RSSI value, etc. These parameters are used to find an optimal route from the source to the destination. Secondly, the proposed approach has enabled devices to learn from the environment and adjust the optimal route-finding formula accordingly. Moreover, these devices and server modules must ensure that every packet is transmitted securely, which is possible only if it is encrypted with an encryption algorithm. For this purpose, a decision tree-enabled device-to-server authentication algorithm is presented where every device and server must take part in the offline phase. Simulation results have verified that the proposed distributed communication approach has the potential to ensure the integrity and confidentiality of data during transmission. Moreover, the proposed approach has outperformed the existing approaches in terms of communication cost, processing overhead, end-to-end delay, packet loss ratio, and throughput. Finally, the proposed approach is adoptable in different networking infrastructures.