Security framework to healthcare robots for secure sharing of healthcare data from cloud

Jain, Saurabh; Doriya, Rajesh

doi:10.1007/s41870-022-00997-8

Cited by 15 publications

(4 citation statements)

References 36 publications

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“…This way, if there were changes to the data, they would be detected. Furthermore, Jain and Doriya [15] introduced a security framework aimed at facilitating the secure download of healthcare data from cloud platforms. Central to their proposed framework was a data auditing mechanism, necessary for ensuring data integrity.…”

Section: Principle Of Cryptographic Hash Functionsmentioning

confidence: 99%

“…Notably, investigations conducted between 2020 and 2023 seemingly explored the utilization of machine learning techniques, particularly the CNN algorithm, for detecting data modification. However, it is noteworthy that only studies [15] and [28] have integrated hash functions into their methodologies, albeit in approaches distinct from what is proposed in our research. Specifically, [15] utilized a hash function to ascertain the completeness and integrity of downloaded files from the cloud, while [28] employed a hash function for the creation of a hash table aimed at content identification.…”

Section: Principle Of Cryptographic Hash Functionsmentioning

confidence: 99%

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Design of an academic document forgery detection system

Boonkrong

2024

Int. j. inf. tecnol.

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Advances in digital technology and communication are reducing the popularity of physical or paper-based documents, particularly in the official academic field. Many academic institutions have shifted from paper-based academic documents to electronic formats. However, with the availability of document-editing and photo-editing software, an adversary can illegally modify original academic documents to suit their needs. Therefore, it is essential that receivers of these digital academic documents must verify the authenticity and detect the forgery of the documents. In this study, a cryptographic hash function, which is concerned with data integrity, is used to detect modifications of digital academic documents. Experiments were conducted to evaluate the correctness and speed of academic document verification. The proposed system was compared with other conventional methods such as digital signature-based, convolutional neural network, and blockchain-based methods. Our study shows that the proposed system can efficiently detect forged digital academic documents with 100% accuracy and a verification speed of 0.352 ms, which is considerably faster than the current digital signature-based, convolutional neural network and blockchain-based technologies. The proposed cryptographic hash function-based method provides another way to help detect forged academic documents.

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Section: Principle Of Cryptographic Hash Functionsmentioning

confidence: 99%

Section: Principle Of Cryptographic Hash Functionsmentioning

confidence: 99%

Design of an academic document forgery detection system

Boonkrong

2024

Int. j. inf. tecnol.

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“…Main solutions to ensure data security in health care include educating healthcare staff about security, restricting access based on roles, implementing data usage controls, monitoring data usage, encrypting data at rest and in transit, securing mobile devices, addressing risks from connected devices, conducting regular risk assessments, maintaining encrypted offline backups, and staying vigilant about software vulnerabilities through patches and updates [200][201][202][203][204][205].…”

Section: Ethical Considerations and Challenges In Implementing Ai And...mentioning

confidence: 99%

Innovative Robotic Technologies and Artificial Intelligence in Pharmacy and Medicine: Paving the Way for the Future of Health Care—A Review

Stasevych,

Zvarych

2023

BDCC

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The future of innovative robotic technologies and artificial intelligence (AI) in pharmacy and medicine is promising, with the potential to revolutionize various aspects of health care. These advances aim to increase efficiency, improve patient outcomes, and reduce costs while addressing pressing challenges such as personalized medicine and the need for more effective therapies. This review examines the major advances in robotics and AI in the pharmaceutical and medical fields, analyzing the advantages, obstacles, and potential implications for future health care. In addition, prominent organizations and research institutions leading the way in these technological advancements are highlighted, showcasing their pioneering efforts in creating and utilizing state-of-the-art robotic solutions in pharmacy and medicine. By thoroughly analyzing the current state of robotic technologies in health care and exploring the possibilities for further progress, this work aims to provide readers with a comprehensive understanding of the transformative power of robotics and AI in the evolution of the healthcare sector. Striking a balance between embracing technology and preserving the human touch, investing in R&D, and establishing regulatory frameworks within ethical guidelines will shape a future for robotics and AI systems. The future of pharmacy and medicine is in the seamless integration of robotics and AI systems to benefit patients and healthcare providers.

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“…Blockchain can be thought of as a record that exists within a system and preserves certain sources of evidence on each end. In other words, a blockchain is an electronic method of data storage that cannot be altered, hacked, or scammed [1]. Blockchain is therefore used to securely store specific types of data, while machine learning is used for other purposes.…”

Section: Introductionmentioning

confidence: 99%

Healthcare Information Exchange Using Blockchain and Machine Learning

Kumar¹,

Albusaidi²,

Halloush³

2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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In several industries, including healthcare, new technologies like blockchain and machine learning are being leveraged to generate innovative solutions. A blockchain network is used in the healthcare system, coupled with machine learning, to store and distribute patient data among hospitals, diagnostic labs, pharmaceutical companies, and clinicians. Applications based on Blockchain, and Machine Learning can precisely identify grave errors, including ones that could be fatal in the healthcare and medical industries. The efficiency, security, and transparency of sharing medical data can therefore be improved. Utilizing these two technologies allows medical institutions to gain knowledge and enhance patient data analysis. In this document, blockchain and Machine Learning were studied and their benefits to the process of the Healthcare Information Exchange Process. The benefits and characteristics of both the technologies and how they help with the process are discussed in a simple way. Blockchain works. Blockchain facilitates decentralized data protection in the healthcare industry and prevents specific risks while Decision-making, patient outcomes, and healthcare professional automation are all aided by Machine Learning in the interchange of healthcare information.

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Security framework to healthcare robots for secure sharing of healthcare data from cloud

Cited by 15 publications

References 36 publications

Design of an academic document forgery detection system

Design of an academic document forgery detection system

Innovative Robotic Technologies and Artificial Intelligence in Pharmacy and Medicine: Paving the Way for the Future of Health Care—A Review

Healthcare Information Exchange Using Blockchain and Machine Learning

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