Homomorphic Encryption Based Privacy-Preservation for IoMT

Salim, Mikail Mohammed; Kim, Inyeung; Doniyor, Umarov; Lee, Chang‐Hoon; Park, Jong Hyuk

doi:10.3390/app11188757

Cited by 32 publications

(10 citation statements)

References 20 publications

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“…Transactional data privacy preservation is the practice of preventing unauthorized users from disclosing personal data while processing it via networks 13 . There are five kinds of privacy-preserving approaches: encryption-based 14 , 15 , perturbation-based 16 , 17 , authentication-based 18 , 19 , differential privacy 20 , 21 , and blockchain-based 22 , 23 . Each of them is addressed individually.…”

Section: Related Workmentioning

confidence: 99%

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Blockchain-based privacy and security model for transactional data in large private networks

Yakubu,

Sabi’u,

Bhattarakosol

2023

Sci Rep

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Cyberphysical systems connect physical devices and large private network environments in modern communication systems. A fundamental worry in the establishment of large private networks is mitigating the danger of transactional data privacy breaches caused by adversaries using a variety of exploitation techniques. This study presents a privacy-preserving architecture for ensuring the privacy and security of transaction data in large private networks. The proposed model employs digital certificates, RSA-based public key infrastructure, and the blockchain to address user transactional data privacy concerns. The model also guarantees that data in transit remains secure and unaltered and that its provenance remains authentic and secure during node-to-node interactions within a large private network. The proposed model has increased the encryption speed by about 17 times, while the decryption process is expedited by 4 times. Therefore, the average overall acceleration obtained was 16.5. Both the findings of the security analysis and the performance analysis demonstrate that the proposed model can safeguard transactional data during communications on large private networks more effectively and securely than the existing solutions.

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Section: Related Workmentioning

confidence: 99%

“…Based on encryption, several privacy-preserving techniques, such as Refs. 14 , 15 , 24 – 26 , have been developed to allow the encryption of data during message exchange. Most schemes rely on symmetric, asymmetric, or homomorphic encryption techniques 27 .…”

Section: Related Workmentioning

confidence: 99%

Blockchain-based privacy and security model for transactional data in large private networks

Yakubu,

Sabi’u,

Bhattarakosol

2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Because the sensors are from diverse vendors, the system’s anonymity isn’t always guaranteed. Furthermore, securing data security while transferring susceptible data from one node to another remains a difficult task [ 204 ]. However, in order to produce information, the data must travel from one end to the other.…”

Section: Addressing and Defending Challenges On Fl With Ai In Healthc...mentioning

confidence: 99%

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

et al. 2022

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Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.

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“…In 2021, HE was combined with secret sharing and computation was performed on the edge computation layer. Moreover, the mathematical operation was conducted in a distributed manner, but no data leakage happened [181]. A recurrent neural network, CryptoRNN, has been introduced recently, which is mostly focused on the privacy preservation of blockchain technology [182].…”

Section: Homomorphic Encryptionmentioning

confidence: 99%

Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning—A Review

Ahsan

Nygard

Gomes

et al. 2022

JCP

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Machine learning is of rising importance in cybersecurity. The primary objective of applying machine learning in cybersecurity is to make the process of malware detection more actionable, scalable and effective than traditional approaches, which require human intervention. The cybersecurity domain involves machine learning challenges that require efficient methodical and theoretical handling. Several machine learning and statistical methods, such as deep learning, support vector machines and Bayesian classification, among others, have proven effective in mitigating cyber-attacks. The detection of hidden trends and insights from network data and building of a corresponding data-driven machine learning model to prevent these attacks is vital to design intelligent security systems. In this survey, the focus is on the machine learning techniques that have been implemented on cybersecurity data to make these systems secure. Existing cybersecurity threats and how machine learning techniques have been used to mitigate these threats have been discussed. The shortcomings of these state-of-the-art models and how attack patterns have evolved over the past decade have also been presented. Our goal is to assess how effective these machine learning techniques are against the ever-increasing threat of malware that plagues our online community.

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Homomorphic Encryption Based Privacy-Preservation for IoMT

Cited by 32 publications

References 20 publications

Blockchain-based privacy and security model for transactional data in large private networks

Blockchain-based privacy and security model for transactional data in large private networks

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning—A Review

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