DNA-AdCrypt: A Carbon-Aware DNA-Inspired Adaptive Encryption Scheme for Energy-Efficient Security of MRI Scans

Medical images such as Magnetic Resonance Imaging (MRI) scans consist of various confidential data that can hamper the privacy of the patient if leaked. Organizations have to abide by various international standards in order to ensure the safe storage and transmission of medical data. Strong encryption is a solution; however, conventional full-image encryption which treats every pixel as equally sensitive results in high computational costs. The resulting energy and carbon emissions pose an environmental challenge. Adaptive selective encryption secures images based on the sensitivity of different portions, resulting in lower carbon emissions. However, these claims have rarely been validated in existing studies covering both real adaptive detection performance and fair energy accounting. To alleviate these issues, this study presents DNA-AdCrypt, a carbon-aware deoxyribonucleic acid (DNA)-inspired tiered encryption system to secure MRI scans. The approach is simulated on brain tumors based on the Ultralytics MRI scan dataset. Fine-tuned You Only Look Once (YOLO) detectors are used to segment the scans into three domains, based on which the strength of the cryptosystem changes. YOLOv8n outperforms other variants, with the highest mAP50 (0.538) and mAP50-95 (0.396). In addition, DNA-Adcrypt reduces the encryption time by up to ≈49% and remains competitive in per-image energy against different simulated ciphers. This study puts forward a design intended to generalize for different modalities in order to promote a greener deployable medical security standard, with brain tumor MRI as an initial validated case.

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Publication Details

Journal
Journal of Cybersecurity and Privacy
Published
2026-09-11
DOI
https://doi.org/10.3390/jcp6050161
Primary Topic
Chaos-based Image/Signal Encryption
Type
article
Field-Weighted Citation Impact
0.00
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article

DNA-AdCrypt: A Carbon-Aware DNA-Inspired Adaptive Encryption Scheme for Energy-Efficient Security of MRI Scans

Manob Jyoti Saikia, Swatisipra Das, Jayanti Rout, Ashutosh Soni et al.
Journal of Cybersecurity and Privacy
Chaos-based Image/Signal Encryption
article

DNA-AdCrypt: A Carbon-Aware DNA-Inspired Adaptive Encryption Scheme for Energy-Efficient Security of MRI Scans

Manob Jyoti Saikia, Swatisipra Das, Jayanti Rout, Ashutosh Soni, Surendra Kumar Nanda, Mrutyunjaya Sathua
article en

Abstract

Medical images such as Magnetic Resonance Imaging (MRI) scans consist of various confidential data that can hamper the privacy of the patient if leaked. Organizations have to abide by various international standards in order to ensure the safe storage and transmission of medical data. Strong encryption is a solution; however, conventional full-image encryption which treats every pixel as equally sensitive results in high computational costs. The resulting energy and carbon emissions pose an environmental challenge. Adaptive selective encryption secures images based on the sensitivity of different portions, resulting in lower carbon emissions. However, these claims have rarely been validated in existing studies covering both real adaptive detection performance and fair energy accounting. To alleviate these issues, this study presents DNA-AdCrypt, a carbon-aware deoxyribonucleic acid (DNA)-inspired tiered encryption system to secure MRI scans. The approach is simulated on brain tumors based on the Ultralytics MRI scan dataset. Fine-tuned You Only Look Once (YOLO) detectors are used to segment the scans into three domains, based on which the strength of the cryptosystem changes. YOLOv8n outperforms other variants, with the highest mAP50 (0.538) and mAP50-95 (0.396). In addition, DNA-Adcrypt reduces the encryption time by up to ≈49% and remains competitive in per-image energy against different simulated ciphers. This study puts forward a design intended to generalize for different modalities in order to promote a greener deployable medical security standard, with brain tumor MRI as an initial validated case.

Journal of Cybersecurity and PrivacyVol. 6(5)
Vivekananda Global University (IN), University of Memphis (US)
Openalex Percentile: Top 13%
Chaos-based Image/Signal Encryption
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