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AI and Big Data for Smart Healthcare: Ensuring Privacy and Security

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 546

Editors


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Guest Editor
School of Information Communication and Technology, Bahrain Polytechnic, Isa Town PO Box 33349, Bahrain
Interests: medical imaging informatics; diagnostic imaging; ambient assisted learning; computer vision
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Canadian Instititute for Cybersecurity, Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
Interests: cybersecurity; natural language processing; edge computing and applications of AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) and big data technologies is revolutionizing modern healthcare, enabling breakthroughs in disease prediction, diagnostics, medical imaging, personalized treatment, and clinical decision-making. By leveraging vast amounts of medical data, AI-driven approaches are reshaping the way healthcare services are designed, delivered, and optimized.

However, with the increasing reliance on sensitive medical data, the need to ensure privacy, security, and trustworthiness has never been greater. Data breaches, adversarial AI attacks, and lack of transparency in decision-making can significantly undermine patient safety and public confidence. Addressing these challenges requires not only algorithmic innovation but also the integration of robust privacy-preserving and security-enhancing frameworks into healthcare solutions.

This Special Issue is dedicated to advancing AI and Big Data for Smart Healthcare, with an essential focus on privacy and security in Smart Healthcare. Contributions should go beyond technical novelty and demonstrate how privacy, security, ethics, and compliance are built into the design and deployment of smart healthcare systems. We encourage submissions that showcase real-world applications such as secure hospital decision-support systems, privacy-aware telemedicine platforms, and resilient AI-based diagnostics.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Machine learning and deep learning approaches for diagnosis
  • Reinforcement learning and generative AI applications in healthcare
  • Large-scale medical data management and analytics
  • Federated learning for distributed healthcare data
  • Secure electronic health records (EHR) sharing
  • Threat modeling, intrusion detection, and anomaly detection in hospital networks
  • Fairness, transparency, and accountability in healthcare AI
  • Secure AI for medical imaging and clinical diagnostics
  • Secure telemedicine and remote patient monitoring systems

Dr. Saqib Iqbal Hakak
Dr. Mamoon Rashid
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning for diagnosis
  • medical data management and analytics
  • big data for smart healthcare
  • privacy and security in smart healthcare

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Published Papers (1 paper)

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Research

35 pages, 1360 KB  
Article
Decentralized Tele-Rehabilitation via Edge AI-Oracle Architecture for Spatiotemporal Pain Assessment
by Nataliya Bilous, Danylo Ostapchenko, Iryna Ahekian and Marcus Frohme
Sensors 2026, 26(13), 4136; https://doi.org/10.3390/s26134136 - 1 Jul 2026
Viewed by 185
Abstract
Remote tele-rehabilitation requires objective pain assessment, but existing approaches fail in two distinct ways. Self-report scales such as the Visual Analog Scale and the Numeric Pain Rating Scale are easy to falsify, opening a special case of the Oracle problem in blockchain-based insurance. [...] Read more.
Remote tele-rehabilitation requires objective pain assessment, but existing approaches fail in two distinct ways. Self-report scales such as the Visual Analog Scale and the Numeric Pain Rating Scale are easy to falsify, opening a special case of the Oracle problem in blockchain-based insurance. Cloud-based computer vision handles falsification but transmits raw biometric video off the patient’s device, violating privacy requirements. A decentralized Edge AI-Oracle architecture is proposed that combines MediaPipe Face Mesh landmark extraction with a recurrent classifier mapping Action-Unit feature sequences to a learned pain score aligned with the Prkachin and Solomon Pain Intensity scale. The recurrent cell is selected empirically across short-context (T = 2) and long-context (T = 120 frames at 24 fps) regimes, with a two-layer Long Short-Term Memory (LSTM) network adopted for deployment. Inference and Elliptic Curve Digital Signature Algorithm (ECDSA) signing run inside an ARM TrustZone Trusted Execution Environment (TEE). Biometric logs are stored off-chain on the InterPlanetary File System (IPFS). Smart contracts anchor results on-chain and open a 24 h optimistic verification window for an off-chain Watchtower auditor. On SynPAIN the LSTM reaches F1 = 0.683 on T = 120 video (leave-one-stratum-out), with a directional but non-significant advantage over Gated Recurrent Unit (GRU) (Wilcoxon p = 0.167). Cross-dataset validation on BioVid Heat Pain Database Part A (87 subjects, 174 paired observations, leave-one-subject-out) yields F1 = 0.519 for LSTM and 0.499 for GRU (Wilcoxon p = 0.549). A processor-only TEE surrogate benchmark estimates 1.96 ms (FP32) and 0.45 ms (INT8) inference latency at T = 120 with a 0.34 MB footprint and 707 µs ECDSA signing latency, leaving the INT8 inference latency more than an order of magnitude below the 33 ms per-frame budget. The dual-layer storage reduces gas costs by a factor of 23.4 (160,261 vs. 3,744,872 gas), corresponding to an illustrative mainnet cost of approximately 0.53 USD per submission at 1 gwei, rising to roughly 16 USD at a busier 30 gwei, and falling to approximately 0.005 USD on Arbitrum One (April 2026 reference parameters), so that continuous monitoring is economically practical on Layer-2. An adaptive-adversary analysis of the Watchtower shows that gross score tampering is detected at every usable operating threshold, whereas a rational adversary who inflates by less than the dispute threshold, or who shapes the injected score to fall just inside it, evades detection. Because the false-positive rate reaches zero only for δ0.15, the protocol bounds rather than eliminates patient-side fraud and motivates a zero-knowledge proof-of-inference successor. The framework is architecturally and economically feasible as a cryptographically verifiable, privacy-preserving tele-rehabilitation substrate aligned with General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) requirements through the Zero-Video Transmission principle, while remaining economically viable under post-Dencun mainnet and Layer-2 conditions. Recognition accuracy on real-world data and robustness to small-magnitude tampering remain limitations that the interchangeable recognition and audit components must improve before clinical deployment. Full article
(This article belongs to the Special Issue AI and Big Data for Smart Healthcare: Ensuring Privacy and Security)
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