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

Year: 2026

Type: article

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.

Source: Journal of Cybersecurity and Privacy

DOI: 10.3390/jcp6050161

Authors: Ashutosh Soni, Jayanti Rout, Mrutyunjaya Sathua, Surendra Kumar Nanda, Swatisipra Das, Manob Jyoti Saikia

Institutions: C.V. Raman Global University, University of Memphis

Language: English

Topic: Chaos-based Image/Signal Encryption

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,207