The rapid growth of digital healthcare systems and connected medical devices has amplified the need for advanced data protection strategies. Traditional security methods face growing challenges from increasing computational power and emerging threats, including the potential impact of quantum computing. This chapter explores the integration of quantum-inspired security and neuromorphic computing as a novel approach for safeguarding sensitive medical data. Quantum-inspired techniques provide strong, future-resistant encryption by leveraging principles such as quantum key distribution and quantum-randomized algorithms, ensuring secure storage, transmission, and access control of patient information. Neuromorphic computing, modelled after the human brain, offers energy-efficient, adaptive, and parallel processing capabilities, making it ideal for edge devices like wearable sensors and Internet of Things enabled medical equipment. By combining these two technologies, healthcare systems can achieve a hybrid framework that enables quantum-resilient encryption, real-time anomaly detection, and adaptive threat monitoring, all with minimal latency and power consumption. This approach not only strengthens patient data privacy and regulatory compliance but also lays the foundation for scalable, intelligent, and secure medical data infrastructures. The chapter further discusses implementation strategies, potential applications, and future directions, highlighting the transformative potential of these emerging technologies in shaping next-generation healthcare security solutions.
The rapid growth of digital healthcare systems and connected medical devices has amplified the need for advanced data protection strategies. Traditional security methods face growing challenges from increasing computational power and emerging threats, including the potential impact of quantum computing. This chapter explores the integration of quantum-inspired security and neuromorphic computing as a novel approach for safeguarding sensitive medical data. Quantum-inspired techniques provide strong, future-resistant encryption by leveraging principles such as quantum key distribution and quantum-randomized algorithms, ensuring secure storage, transmission, and access control of patient information. Neuromorphic computing, modelled after the human brain, offers energy-efficient, adaptive, and parallel processing capabilities, making it ideal for edge devices like wearable sensors and Internet of Things enabled medical equipment. By combining these two technologies, healthcare systems can achieve a hybrid framework that enables quantum-resilient encryption, real-time anomaly detection, and adaptive threat monitoring, all with minimal latency and power consumption. This approach not only strengthens patient data privacy and regulatory compliance but also lays the foundation for scalable, intelligent, and secure medical data infrastructures. The chapter further discusses implementation strategies, potential applications, and future directions, highlighting the transformative potential of these emerging technologies in shaping next-generation healthcare security solutions.
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.
As developments in quantum computing threaten to break existing encryption techniques safeguarding sensitive patient information in cloud-based medical systems, the healthcare sector is confronting never-before-seen cryptographic difficulties. Addressing unique needs like multi-decade data retention demands, legacy medical device limitations, and real-time patient care demands, the framework provides a specialized quantum-secure cryptographic design created particularly for healthcare settings. Three innovations are at the basis: a tiered implementation plan that dynamically allocates post-quantum protection level by medical data sensitivity categorisation, application to edge computing architecture that allows quantum-resistant security with resource-constrained medical devices without hardware enhancements, and healthcare interoperability-aware hybrid protocols to ensure continuous data transfer between FHIR, HL7, and DICOM standards in the event of cryptographic switchovers. The model uses NIST-standardized post-quantum algorithms such as CRYSTALS-Kyber and CRYSTALS-Dilithium (digital signatures) and SPHINCS+ (long-term integrity verification) tailored to healthcare with competing requirements of the highest security and lowest operations latency. The implementation strategies will include the application of phase-based implementation approaches that will include the infrastructure preparation phase through full integration of the ecosystem, with a special focus on the optimization of HIPAA compliance and alignment of regulatory frameworks. Performance optimization techniques leverage characteristic healthcare data access patterns, including patient-centric workflows, clinical department batch processing, and medical imaging parallelization strategies. The quantum-safe infrastructure establishes foundational capabilities for next-generation healthcare innovations, including precision medicine genomics platforms, federated artificial intelligence systems, and global health collaboration networks requiring cryptographic security guarantees spanning decades or centuries against both classical and quantum computational threats.
The rapid evolution of quantum computing puts traditional encryption techniques at risk of becoming ineffective, potentially exposing sensitive healthcare information to unprecedented cyber vulnerabilities. This research proposes the implementation of quantum-driven cryptographic framework, which utilizes quantum key distribution, quantum-safe algorithms, and random high entropy quantum number generators to ensure the safety of healthcare data on different health information systems. This framework advocates for the unification of EHRs, telemedicine, and IoT-enabled devices. This incorporation defends not only against current cyber vulnerabilities, but also future sequential cyber risks. This is crucial in protecting patient trust within the healthcare system. The goal is to develop a protected healthcare crypto framework best described as box-quantum safe, scalable and dynamically compliant with respective health laws cryptographically enclosing the sensitive data against relational and quantum attacks. The framework prioritizes patient data accessibility with seamless security with the incorporation of quantum key distribution, quantum safe algorithms, and entropy quantum number generators. The framework also facilitates uninterrupted incorporation with EHRs, telemedicine and connected devices while assuring continuous compliance with prevailing data protection legislations. The quantum driven frameworks to cryptographic systems are highly beneficial in safeguarding sensitive data, as it offers robust protection from current and emerging cyber threats. Integrating qsafe encryption and modern healthcare data management systems enables the proposed system to safeguard the patient's privacy, anger no international security compliance issues, and be resilient to emerging quantum threats.
Because many modern healthcare security systems rely on traditional cryptographic primitives that are susceptible to quantum attacks, the quick development of quantum computing poses previously unheard-of risks. With the increasing use of wearable sensors, telemedicine, AI-driven diagnostics and distributed clinical databases, as well as the increasing digitisation of healthcare, the need for quantum-resilient protection becomes critical. The integration of quantum and post-quantum cryptography (PQC) as fundamental security measures for upcoming intelligent healthcare ecosystems is thoroughly examined in this chapter. We summarise recent developments in cryptographic standards, secure communication protocols, AI-enabled defence mechanisms and blockchain-based data integrity systems using a PRISMA-guided systematic review. With an emphasis on performance, scalability and resistance to Shor- and Grover-based attacks, we further develop a simulation framework to assess hybrid quantum, classical security architectures under adversarial quantum scenarios. Our results show that while hash-based signatures provide ultra-long-term integrity for genomic and archival medical data, lattice-based PQC schemes, in particular CRYSTALS-Kyber and Dilithium, show strong suitability for electronic health record (EHR) encryption, secure IoMT communication and federated learning aggregation. A unified quantum-resilient architecture incorporating PQC, blockchain consensus, AI-based anomaly detection and quantum key distribution (QKD) is one of the theoretical contributions. In summary, we highlight the potential of quantum homomorphic AI, neuromorphic cryptosystems, quantum biometrics and adaptive threat intelligence to develop a safe, reliable and scalable paradigm for quantum-ready healthcare.
In the rapidly emerging digital age, sensitive information has been transformed through medical devices in the healthcare industry and network-connected, leading to an increase in multiple security issues and challenges. Conventional techniques are facing vulnerabilities due to advances in computing, making quantum-resistant solutions critical for protecting sensitive patient data, medical devices, and healthcare networks. However, executing a quantum security framework in a healthcare situation presents challenges. These Conventional cryptographic techniques are significantly vulnerable to the development of quantum computing capabilities, which requires a transition toward a quantum security framework.
As the digital transformation accelerates across industries, the need for robust data protection mechanisms has never been more critical. Sensitive data, particularly in sectors like healthcare, finance, and government, is constantly at risk of exposure due to increasing cyber threats. Traditional encryption techniques, while effective, often fall short when dealing with large-scale data and complex threats. This paper explores the intersection of privacy-preserving techniques, quantum computing, and artificial intelligence (AI) to enhance data protection. We propose the integration of quantum-enhanced AI frameworks that combine quantum computing's unique capabilities with AI-driven privacy-preserving mechanisms. These systems can not only safeguard sensitive data but also improve the efficiency of encryption and decryption processes. The paper discusses the potential of quantum-enhanced AI in creating more secure, scalable, and transparent data protection strategies, while addressing the challenges of implementing such advanced technologies, including computational complexity and ethical considerations. By leveraging quantum algorithms and AI techniques such as homomorphic encryption and differential privacy, the proposed solution aims to provide a futureproof approach to protecting sensitive information from increasingly sophisticated threats.