Healthcare systems are rapidly becoming digital, which has increased the demand for safe, dependable, and privacy-preserving authentication methods. Iris biometrics have emerged as a key component for identity verification in delicate settings due to their high accuracy and stability. However, protecting privacy and data security is a difficulty for conventional biometric systems, particularly in light of new quantum risks. For advanced healthcare security, this study suggests a revolutionary quantum-enhanced, privacy-preserving iris biometrics system. Through the integration of quantum computing technologies, the framework makes use of quantum-accelerated algorithms like Grover's algorithm for efficient template matching and quantum-safe cryptography like Quantum Key Distribution (QKD). Homomorphic encryption and federated learning are two further ways to improve privacy preservation while guaranteeing adherence to HIPAA and other healthcare data protection laws. In comparison to conventional techniques, the suggested system performs better in terms of accuracy, security, and computational efficiency when tested on benchmark iris datasets. By filling important gaps in safe and privacy-focused identity management, this research creates a novel method at the nexus of quantum computing, biometrics, and healthcare.
Healthcare systems are rapidly becoming digital, which has increased the demand for safe, dependable, and privacy-preserving authentication methods. Iris biometrics have emerged as a key component for identity verification in delicate settings due to their high accuracy and stability. However, protecting privacy and data security is a difficulty for conventional biometric systems, particularly in light of new quantum risks. For advanced healthcare security, this study suggests a revolutionary quantum-enhanced, privacy-preserving iris biometrics system. Through the integration of quantum computing technologies, the framework makes use of quantum-accelerated algorithms like Grover's algorithm for efficient template matching and quantum-safe cryptography like Quantum Key Distribution (QKD). Homomorphic encryption and federated learning are two further ways to improve privacy preservation while guaranteeing adherence to HIPAA and other healthcare data protection laws. In comparison to conventional techniques, the suggested system performs better in terms of accuracy, security, and computational efficiency when tested on benchmark iris datasets. By filling important gaps in safe and privacy-focused identity management, this research creates a novel method at the nexus of quantum computing, biometrics, and healthcare.
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.
Ensuring data security and privacy is highly pertinent in today’s era marked by the dominance of collaborative machine learning. This study presents a novel framework, named" Quantum-Resilient Privacy-Preserving Federated Learning for Enhanced Data Security," which leverages quantum computing to strengthen federated learning models against adversarial risks and enhance privacy safeguards. Our research investigates the incorporation of quantum key distribution and entanglement protocols into federated learning, therefore establishing a quantum- resilient methodology. The findings demonstrate the model’s ability to effectively resist advanced threats, hence assuring the maintenance of strong data security measures. The utilization of quantum technology in this context serves to strengthen the fundamental principles of cryptography, while simultaneously introducing a novel approach to enhancing privacy. Moreover, the study explores the potential of the framework to accommodate various datasets from different cooperating entities, while also resolving the difficulties presented by the diversity of the data. The framework’s success in accommodating multiple machine learning models and applications is demonstrated by evaluations of scalability and computing efficiency. In essence, the research paper titled" Quantum-Resilient Privacy-Preserving Federated Learning for Enhanced Data Security" represents a significant progression in the field of safeguarding collaborative machine learning. The quantum-resilient paradigm not only increases privacy but also offers a safe foundation for the future of collaborative machine learning. The issue at hand pertains to the growing apprehensions over the safeguarding of data privacy and security within the context of our technologically advanced day.
The development of national digital identity systems has sped up, leading to an increasing demand for biometric authentication system, which needs to be secure and able to serve all users and be adapted to future needs. The classical biometric systems rely on fingerprints, iris scans and facial recognition technology, which poses significant accessibility challenges to individuals with physical disabilities, visual impairments and atypical biometric characteristics such as those born without fingers or having more fingers. The existing protocols are vulnerable to the new quantum attacks that threaten classical cryptographic protocols. This work presents the Quantum-Resilient Inclusive Biometric Framework, which allows the use of various biometrics and resilient to quantum threats. The framework supports matching via a quantum state encoding method which encodes multiple biometric features under a Grover-based search technique that amplifies the amplitudes. We present a smart identity card system which can be proved to be post-quantum secure by combining lattice-based cryptography and quantum-safe digital signatures. The architecture is such that offering access to the chip-enabled authentication and Braille-assisted verification system doesn't compromise the security of the cryptographic protections. Security is evaluated by calculating quantum threat models with Shor-type, Grover-type attacks as potential adversary techniques. The proposed model offers better scalability for large-scale biometric search, as well as it offers better accessibility to the disabled people and better privacy and security in facing cyber threats that will be evolved by quantum technologies in the future. The work builds a secure and affordable national identity infrastructure through design of national identity systems based on quantum computing principles for the inclusive biometric systems.
Biometric authentication has become integral to digital identity systems, particularly in smart cities where it enables secure access to services across governance, transportation, and public infrastructure. Centralised architectures, though widely used, pose privacy and scalability challenges due to the aggregation of sensitive biometric data. Decentralised identity frameworks offer better data sovereignty and eliminate single points of failure but introduce new security concerns, particularly around mutual trust among distributed devices. In such environments, biometric sensors and verification agents must authenticate one another before sharing sensitive biometric data. Existing authentication schemes rely on classical public key infrastructure, which is increasingly susceptible to quantum attacks. This work addresses this gap by proposing a quantum-secure communication protocol for decentralised biometric systems, built upon an enhanced Quantum Key Distribution (QKD) system. The protocol incorporates quantum-resilient authentication at both the classical and quantum layers of QKD: post-quantum cryptography (PQC) is used to secure the classical channel, while authentication qubits verify the integrity of the quantum channel. Once trust is established, QKD generates symmetric keys for encrypting biometric data in transit. Qiskit-based simulations show a key generation rate of 15 bits/sec and 89% efficiency. This layered, quantum-resilient approach offers scalable, robust authentication for next-generation smart city infrastructures.
In digital healthcare systems, there has been an upsurge in the utilization of Electronic Health Records (EHRs) which is raising some concerns on data privacy and security, particularly following the quantum computing innovation. RSA and ECC encryption services, which are the traditional mechanisms, are expected to be rendered useless in light of quantum algorithms, like that of Shor and Grover, which can break classical cryptographic algorithms. This paper introduces a framework of quantum enhanced data security to solve these vulnerabilities, in which Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD) are integrated. The suggested model uses the lattice-based encryption (Kyber), post-quantum digital signature (Di lithium), and QKD to secure key exchange. A comparative simulation to classical encryption approaches manifests that the model with innovative frameworks provides the high levels of resilience toward quantum penetrations, a better authentication accuracy and a slight augmentation of access durability. It indicates that the combination of PQC and QKD could be a scalable, persistent, and future-proof solution to the security of sensitive healthcare information under next-generation cyberattacks.
Secure, privacy-preserving sharing and analysis of health data is foundational to precision medicine, population health, and telehealth services. Emerging quantum networks promise fundamentally new communication primitives (entanglement, quantum key distribution) that when integrated with advanced AI architectures can enable end-to-end secure healthcare systems with improved confidentiality, tamper resistance, and novel distributed computation modes. This article develops a systematic framework for integrating quantum communications and cryptography with classical and hybrid quantum–classical AI models in healthcare. We (a) summarize the enabling quantum network technologies and AI building blocks, (b) propose layered system architectures and protocols for secure data exchange and collaborative learning, (c) formalize threat models and privacy requirements in healthcare contexts, (d) discuss algorithmic integrations (federated learning with QKD, quantum-enhanced machine learning, secure multiparty quantum protocols), (e) provide evaluation metrics and prototype deployment roadmaps, and (f) identify research gaps and regulatory implications. The treatment is scholarly yet practical, aimed at researchers, engineers, and healthcare stakeholders planning future-proof secure AI systems. (Keywords: quantum networks; quantum key distribution; quantum machine learning; federated learning; healthcare security; privacy.)