Design of Smart and Secured Healthcare Service Using Deep Learning with Modified SHA-256 Algorithm

Mohanty, Mohan Debarchan; Das, Abhishek; Mohanty, Mihir Narayan; Altameem, Ayman; Nayak, Soumya Ranjan; Saudagar, Abdul Khader Jilani; Poonia, Ramesh Chandra

doi:10.3390/healthcare10071275

Cited by 5 publications

(5 citation statements)

References 35 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The smart healthcare system presented by Mohanty et al [114] incorporates the capability of tumor detection from brain MRI images using CNN-LSTM and additionally the proposed system is secured by using the SHA-256 encryption technique. In the suggested smart healthcare system, the modified SHA-256 algorithm is recommended for safe medical data processing.…”

Section: Encryption Algorithms For Deep Learning-based Medical Image ...mentioning

confidence: 99%

“…The efficacy of deep learningbased medical image analysis is demonstrated by a variety of recently published literature [12,[37][38][39][40][41]49,[49][50][51][52][53][54][55][56][57][58][59][60][61][62]64,64,65,[157][158][159]. In the literature, several deep learning architectures have been described to handle various imaging modalities and tasks related to medical image processing with different components of encryption or cryptography [107,110,[112][113][114][115][116]123,124,135,136,140,141,144,[147][148][149][150][151]160,161]. ...…”

Section: Various Deep Learning Architectures With Security Features F...mentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Medical Image Cryptography: A Comprehensive Review

Lata

Cenkeramaddi

2023

Applied Sciences

View full text Add to dashboard Cite

Electronic health records (EHRs) security is a critical challenge in the implementation and administration of Internet of Medical Things (IoMT) systems within the healthcare sector’s heterogeneous environment. As digital transformation continues to advance, ensuring privacy, integrity, and availability of EHRs become increasingly complex. Various imaging modalities, including PET, MRI, ultrasonography, CT, and X-ray imaging, play vital roles in medical diagnosis, allowing healthcare professionals to visualize and assess the internal structures, functions, and abnormalities within the human body. These diagnostic images are typically stored, shared, and processed for various purposes, including segmentation, feature selection, and image denoising. Cryptography techniques offer a promising solution for protecting sensitive medical image data during storage and transmission. Deep learning has the potential to revolutionize cryptography techniques for securing medical images. This paper explores the application of deep learning techniques in medical image cryptography, aiming to enhance the privacy and security of healthcare data. It investigates the use of deep learning models for image encryption, image resolution enhancement, detection and classification, encrypted compression, key generation, and end-to-end encryption. Finally, we provide insights into the current research challenges and promising directions for future research in the field of deep learning applications in medical image cryptography.

show abstract

Section: Encryption Algorithms For Deep Learning-based Medical Image ...mentioning

confidence: 99%

Section: Various Deep Learning Architectures With Security Features F...mentioning

confidence: 99%

Deep Learning for Medical Image Cryptography: A Comprehensive Review

Lata

Cenkeramaddi

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Pham, Hoai Luan, et al [35], 2022: introduced the Multimode SHA-2 Accelerator (MSA) for SHA-2 cryptographic functions, emphasizing performance, efficiency, and flexibility, including support for various cryptographic hash algorithms. Mohanty, Mohan Debarchan, et al [36], 2022: presented a modified SHA-256 encryption algorithm for securing medical insurance data, enhancing security and efficiency in healthcare data management. Suhaili, et al [37], 2022: focused on optimizing the SHA-256 hash function with unfolding techniques to improve throughput.…”

Section: Discussion and Comparisonmentioning

confidence: 99%

“…This is done in order to accommodate the ever-evolving requirements of blockchain mining and data security applications. Mohanty, Mohan Debarchan, et al [36], 2022, presented a modified SHA-256 encryption algorithm to enhance security in medical insurance data processing, mitigating fraud risks. The modified algorithm generated a long key and combined it for robust encryption, applicable to both insurance and medical data.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

A State-of-the-Art Survey for IoT Security and Energy Management based on Hashing Algorithms

Teba Mohammed Ghazi Sami

2023

IJRITCC

View full text Add to dashboard Cite

The Internet of Things (IoT) has developed as a disruptive technology with wide-ranging applications across several sectors, enabling the connecting of devices and the acquisition of substantial volumes of data. Nevertheless, the rapid expansion of networked gadgets has generated substantial apprehensions pertaining to security and energy administration. This survey paper offers a detailed examination of the present state of research and advancements in the field of Internet of Things (IoT) security and energy management. The work places special emphasis on the use of hashing algorithms in this context. The security of the Internet of Things (IoT) is a crucial element in safeguarding the confidentiality, integrity, and availability of data inside IoT environments. Hashing algorithms have gained prominence as a fundamental tool for enhancing IoT security. This survey reviews the state of the art in cryptographic hashing techniques and their application in securing IoT devices, data, and communication. Furthermore, the efficient management of energy resources is essential to prolong the operational lifespan of IoT devices and reduce their environmental impact. Hashing algorithms are also instrumental in optimizing energy consumption through data compression, encryption, and authentication. This survey explores the latest advancements in energy-efficient IoT systems and how hashing algorithms contribute to energy management strategies. Through a comprehensive analysis of recent research findings and technological advancements, this survey identifies key challenges and open research questions in the fields of IoT security and energy management based on hashing algorithms. It provides valuable insights for researchers, practitioners, and policymakers to further advance the state of the art in these critical IoT domains.

show abstract

“…Reference [26] suggested a novel algorithm for the decision making with neural network and deep learning. Reference [28] suggested the design of a Smart and Secured Healthcare Service blockchain with the use of Deep Learning. Similar algorithm can be applied the module here as well.…”

Section: Decision Making At Cloud Layermentioning

confidence: 99%

Enabling Blockchain with IoMT Devices for Healthcare

et al. 2022

View full text Add to dashboard Cite

Significant modifications have been seen in healthcare facilities over the past two decades. With the use of IoT-enabled devices, the monitoring and analysis of patient diagnostic parameters is made considerably easy. The new technology shift for medical field is IoMT. However, the problem of privacy for patient data and the security of information still a point to ponder. This research proposed a prototype model to integrate the blockchain and IoMT for providing better analysis of patient health factors. The authors suggested IoMT data to be collected over Edge Computing gateway devices and forward to Cloud Gateway. The three-layered decision making structure ensures the integrity of the data. The further analysis of information collected over sensor-based devices is done in the Cloud IoT Central Hub service. To ensure the secrecy and compliance of the patient data, Smart Contracts are integrated. After the exchange of smart contracts, a block of information is broadcast over the health blockchain. The P2P network makes it viable to retain all health statistics and further processing of information. The paper describes the scenario and experimental setup for a COVID-19 data-set analyzed in the proposed prototype mode. After the collection of information and decision making, the block of data is sent across all peer nodes. Thus, the power of IoMT and blockchain makes it easy for the healthcare worker to diagnose and handle patient data with privacy. The IoMT is integrated with artificial intelligence to enable decision making based on the classification of data. The results are saved as transactions in the blockchain hyperledger. This study demonstrates the prototype model with test data in a testing network with two peer nodes.

show abstract

Design of Smart and Secured Healthcare Service Using Deep Learning with Modified SHA-256 Algorithm

Cited by 5 publications

References 35 publications

Deep Learning for Medical Image Cryptography: A Comprehensive Review

Deep Learning for Medical Image Cryptography: A Comprehensive Review

A State-of-the-Art Survey for IoT Security and Energy Management based on Hashing Algorithms

Enabling Blockchain with IoMT Devices for Healthcare

Contact Info

Product

Resources

About