ArticleScientific reports2026
Reversible data hiding for electronic patient information security for telemedicine applications.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A New Home-Based Upper- and Lower-Limb Telerehabilitation Platform with Experimental Validation.Arabian journal for science and engineering · 2023Article
- A Machine Learning Approach for Monitoring and Classifying Healthcare Data-A Case of Emergency Department of KSA Hospitals.International journal of environmental research and public health · 2023Article
- A Deep-Learning-Based Collaborative Edge-Cloud Telemedicine System for Retinopathy of Prematurity.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Telemedicine workflows require the secure transmission of medical images while preserving diagnostic integrity. We propose a Reversible Data Hiding in Encrypted Images (RDH‑EI) scheme tailored for telemedicine that couples (i) a Generation of Encryption Parameters (GEP) mechanism to derive per‑block embedding controls from a data‑hiding key and (ii) a two‑level Least Significant Bit (LSB) strategy that provides separable payload extraction and exact image recovery. Images are first encrypted using AES‑CTR with a unique nonce; GEP then produces block‑wise traversal orders, offsets, and parity masks used by Phase‑1 (odd blocks, parity‑of‑triples) and Phase‑2 (even blocks, serialized side‑information). The method supports three operating modes: payload‑only extraction, decrypt‑only viewing, and full, bit‑exact recovery when both keys are available. On a 90‑image test set (30 X‑rays, 30 MRIs, 30 CTs, all 512 × 512), the proposed approach achieves higher quality on directly decrypted images than representative baselines at Z = 16: PSNR 39.92 ± 0.41 dB (X‑ray), 37.78 ± 0.38 dB (MRI), and 38.27 ± 0.36 dB (CT), with SSIM 0.9784 ± 0.0021, 0.9694 ± 0.0023, and 0.9835 ± 0.0018, respectively. The recovered images are bit‑exact (PSNR = ∞). Encryption robustness is supported by near‑maximal entropy, high NPCR (> 99%), and UACI near 33%. These results indicate that the proposed GEP‑driven, two‑level embedding improves the payload-distortion trade‑off while retaining strict reversibility, making it suitable for secure medical‑image sharing in telemedicine.
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