Abstract The Internet of Healthcare Things is essential for enhancing people's protection, care, and health. Health-related criteria for patients can be remotely tracked and transferred to medical data centers via cloud storage, saving them the trip to the hospital. Additionally, the volume of data processed by Internet of Healthcare Things devices is growing exponentially. Due to the increased revelation of sensitive information, there are many unresolved issues about data security and privacy gathered through Internet of Healthcare Things devices. The requirement to apply machine learning algorithms to massive industrial data is developing as quickly as categorization methods themselves. We offer an Entangled Fuzzy Logic based Quantum Key Distribution for data protection to improve and assess the degree of security such breaches, data integrity, etc. in order to safely store and handle this information in the cloud. Results show that the proposed method enhancing the level of security to the cloud more than 99%, the works remarkably well.
Abstract The Internet of Healthcare Things is essential for enhancing people's protection, care, and health. Health-related criteria for patients can be remotely tracked and transferred to medical data centers via cloud storage, saving them the trip to the hospital. Additionally, the volume of data processed by Internet of Healthcare Things devices is growing exponentially. Due to the increased revelation of sensitive information, there are many unresolved issues about data security and privacy gathered through Internet of Healthcare Things devices. The requirement to apply machine learning algorithms to massive industrial data is developing as quickly as categorization methods themselves. We offer an Entangled Fuzzy Logic based Quantum Key Distribution for data protection to improve and assess the degree of security such breaches, data integrity, etc. in order to safely store and handle this information in the cloud. Results show that the proposed method enhancing the level of security to the cloud more than 99%, the works remarkably well.
Abstract The medical internet of things (MIoTs) is essential for enhancing people's protection, care, and health. Health-related criteria for patients can be remotely tracked and transferred to medical data centers via cloud storage, saving them the trip to the hospital. Additionally, the volume of data processed by MIoTs devices is growing exponentially. Due to the increased revelation of sensitive information, there are many unresolved issues about data security and privacy gathered through MIoTs devices. The requirement to apply machine learning algorithms to massive industrial data is developing as quickly as categorization methods themselves. We offer an Entangled Fuzzy Logic based Quantum Key Distribution (EFL-QKD) for data protection to improve and assess the degree of security such breaches, data integrity, etc. in order to safely store and handle this information in the cloud. Results show that the proposed method enhancing the level of security to the cloud more than 99%, the works remarkably well.
The Internet of Things (IoT) has transformed healthcare, facilitating remote patient monitoring, enhanced medication adherence, and chronic disease management. However, this interconnected ecosystem faces significant vulnerabilities with the advent of quantum computing, which threatens to break existing encryption standards protecting sensitive patient data in IoT-enabled medical devices. This chapter examines the quantum threat to healthcare IoT security, highlighting the potential impacts of compromised encryption, including privacy breaches, device failures, and manipulated medical records. It introduces post-quantum cryptography (PQC) and quantum-resistant techniques like quantum key distribution (QKD), addressing their application in resource-constrained healthcare IoT devices such as pacemakers, monitoring tools, and telemedicine systems. The chapter further explores the challenges of integrating these solutions and reviews global efforts in mitigating quantum risks, offering insights into suitable PQC primitives for various healthcare use cases.
The Internet of Things (IoT) revolutionised healthcare by enabling remote patient monitoring, improved medication adherence, and chronic disease management. However, this interconnectedness creates vulnerabilities, especially with the looming threat of quantum computing. Quantum computers hold the power to break current encryption standards, safeguarding sensitive patient data in IoT-enabled medical devices. This chapter explores the looming quantum threat to healthcare IoT security. It delves into the potential consequences of compromised encrypted data, including privacy breaches, device malfunctions, and manipulated medical records. To address these issues, the chapter introduces post-quantum cryptography (PQC) and other quantum-resistant techniques (QKD) alongside their key considerations. It analyses the resource constraints (storage, processing power) of typical healthcare IoT devices – pacemakers, monitoring tools, and telemedicine equipment – to determine suitable PQC primitives for different use cases. The chapter then discusses the challenges of implementing these techniques and explores ongoing mitigation projects worldwide.
Cloud computing has emerged as the prevailing development in healthcare systems across the global market. These systems are creating and processing vast amounts of patient data that require a certain level of security. However, due to the introduction of quantum computation, the future of cryptographic techniques on which Cloud security relies is in danger. The following paper seeks to explore the compatibility of quantum computing and cloud security and regard to the protection of health data. It also includes a comprehensive analysis of the current risks, a discussion of already existing quantum-vulnerable points, and a strategy for creating a quantum-safe strategy for safe patient data storage in healthcare. The study under consideration also employs quantum cryptography and cloud structures to identify threats and create appropriate defence mechanisms. Some models explored and analyzed include Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC) and hybrid cryptosystems. A simulated hospital database has brought about the fragility of some of these algorithms, a research work dubbed as quantum resilience, in order to explain how it is possible to integrate these two concepts without removing the aspects of the cloud that make it appealing to many people, including scalability and accessibility. This indicates that there has been a major enhancement in standing against quantum attacks, specifically showing the way towards effective, sustainable and protected healthcare information systems.
The emergence and advancement of technologies such as the Internet of Medical Things (IoMT), Electronic Health Records (EHR), and Smart Medical Gate (SMG) have remarkably changed patient care practices. With the digitization of healthcare services, concerns regarding data security have increased. These systems face increasing risks due to cyber threats and the advances of quantum computing technology. For instance, Peter Shor’s quantum algorithms are predicted to affect the integrity and confidentiality of sensitive medical data. This puts classical (non-quantum) cryptographic systems such as RSA (Ron Rivest, Adi Shamir, and Leonard Adleman) and ECC (Elliptic Curve Cryptography) at risk. This work proposes an integrated high-dimensional quantum key distribution (HD-QKD) infrastructure for secure medical data transmission across IoMT, EHR, and SMG ecosystems. It introduces a cloud-based Central EHR/Cloud Server for key management, along with edge Quantum Security Gateways. The system employs qudit encoding (d > 2) over a 50 km optical fiber link with 12-dB attenuation. Its edge-centric design ensures noise resilience and delivers a high information rate per photon. It also provides low-latency security against quantum threats while maintaining compatibility with existing fiber networks through wavelength division multiplexing. Simulations validate the system's potential, achieving secure key rates of 2.5 megabits per second between medical structures-double the rate of prior qubit-based Quantum Key Distribution (QKD) protocols-demonstrating superior scalability and performance for real-time healthcare applications.
Abstract The amount and difficulty of data have both grown a lot since the number of Internet of Things (IoT) devices in the medical and industrial sectors has grown a lot. These situations with a lot of data have needs that go beyond what most cloud and edge computing frameworks can do when it comes to processing in real time, energy efficiency, and security. The inclusion of quantum computing, which can handle a lot of processing at once and execute more sophisticated tasks at once, could transform the way things are done in a big manner. When it comes to handling sensitive and different IoT, the current Edge-Cloud designs have a few big problems: they don't scale effectively, they have a lot of latency, and they do not have enough capacity. In order for predictive maintenance and automation to perform well in industrial Internet of Things (IIoT) contexts, they need to be able to address problems and act promptly. On the other hand, late diagnoses and data breaches are very dangerous for medical settings. All of these key factors; the computing burden, the reaction speed, and the security are handled by one unified design. The Quantum-Enhanced Edge-Cloud Computing Framework (QEECF) is proposed to speed up essential operations including encryption, finding anomalies, and making decisions in real time. With this technology, the quantum processing units (QPUs) are added to both the cloud servers and the edge nodes at the same time. The framework uses a hybrid quantum-classical scheduling strategy that combines quantum annealing with variational circuits to give the best possible job offloading. The objective of a federated quantum learning system is to keep sensitive data safe. This data is stored in databases all around the world, like those used in business and healthcare. We evaluate the technology by running simulations of things like smart manufacturing and keeping an eye on healthcare. These simulations are easier using IBM Qiskit and edge simulators. When compared to standard Edge-Cloud arrangements, QEECF might increase data throughput by 36%, cut job delays by 43%, and improve the accuracy of finding anomalies by 48%.