Deep Learning and Cryptographic Techniques for Secure Medical X-Ray Imaging: A Comprehensive Review

Abstract

Medical X-ray imaging is an important component of early disease detection, clinical diagnosis, and evaluation of the treatment. But when combined with digital storage and use of cloud services, and the analysis being done with AI, this creates a strong fear of the loss of patient privacy, the integrity of data, and of how to safely share sensitive health records. Conventional forms of protection like anonymization and simple encryption can no longer be used to respond to highly developed cyberattacks and adversary challenges. Thus, the combination of deep learning and the further evolution of the cryptographic technologies has become a potential solution to guarantee confidentiality, integrity, and reliability in the imaging of medical X-rays. The review entails a synthesis of state of the art methods at the nexus of deep learning and cryptography. We combine existing solutions into four levels: (i) privacy-preserving learning, i.e. federated learning, differential privacy, homomorphic encryption, and secure multi-party computation; (ii) content protection, i.e. selective encryption, chaos based lightweight cryptography, and reversible watermarking of provenance; (iii) trustful and robust inference, addressing adversarial and robustness, model watermarking and calibration to reliably deploy; and (iv) governance and audit, i.e. blockchain based provenance tracing and role. As a metric of these methods, we talk about benchmark datasets (e.g., NIH ChestX-ray14, CheXpert, MIMIC-CXR), security metrics (confidentiality, integrity, availability, accountability), diagnostic fidelity measures (AUROC, sensitivity, specificity) and cryptographic overheads (latency, throughput, privacy budgets). It is not only challenging approaches that are strong, but our analysis reveals the shortcomings of the current approaches: homomorphic encryption and certified robustness are computationally intensive to be applied in clinical practice, whereas the former, federated learning, and the latter, watermarking, are already ready to be reported as operational in clinic conditions. Among the most important research gaps are scalable, encrypted inference, composable privacy-robustness trade-offs, secure generation of privacy-preserving synthetics, and regulatory-permissible benchmarks in line with regulations, like HIPAA and GDPR. The deep learning cryptography connection as presented in this review can help forward the discussion of security-conscious medical imaging pipelines that safeguard patient data without affecting clinical utility as prerequisites to building the responsible use of AI in providing healthcare.