Application of Robust Zero-Watermarking Scheme Based on Federated Learning for Securing the Healthcare Data

Han, Baoru; Jhaveri, Rutvij H.; Wang, Han; Qiao, Dawei; Du, Jinglong

doi:10.1109/jbhi.2021.3123936

Cited by 48 publications

(27 citation statements)

References 31 publications

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“…In addition, the authors proposed an FL algorithm that identifies and rejects erroneous clients while achieving an accuracy close to FL without erroneous clients. Furthermore, Han et al [38] proposed a zero-watermarking scheme based on FL in order to solve the privacy and security issues of the teledermatology healthcare framework.…”

Section: Fl For Internet Of Medical Thingsmentioning

confidence: 99%

Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

Aouedi

Sacco

Piamrat

et al. 2023

IEEE J. Biomed. Health Inform.

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Recent medical applications are largely dominated by the application of Machine Learning (ML) models to assist expert decisions, leading to disruptive innovations in radiology, pathology, genomics, and hence modern healthcare systems in general. Despite the profitable usage of AI-based algorithms, these data-driven methods are facing issues such as the scarcity and privacy of user data, as well as the difficulty of institutions exchanging medical information. With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types. To solve these issues, Federated Learning (FL) appeared as a valuable approach in the medical field, allowing patient data to stay where it is generated. Since an FL setting allows many clients to collaboratively train a model while keeping training data decentralized, it can protect privacy-sensitive medical data. However, FL is still unable to deliver all its promises and meets the more stringent requirements (e.g., latency, security) of a healthcare system based on multiple Internet of Medical Things (IoMT). For example, although no data are shared among the participants by definition in FL systems, some security risks are still present and can be considered as vulnerabilities from multiple aspects. This paper sheds light upon the emerging deployment of FL, provides a broad overview of current approaches and existing challenges, and outlines several directions of future work that are relevant to solving existing problems in federated healthcare, with a particular focus on security and privacy issues.

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Section: Fl For Internet Of Medical Thingsmentioning

confidence: 99%

Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

Aouedi

Sacco

Piamrat

et al. 2023

IEEE J. Biomed. Health Inform.

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“…Jemmali studied the nonlinear complex relationship between blasting parameters and their main influencing factors [28]. Han thinks that it is adaptive to the establishment of the relationship model between the blasting parameters and the main influencing factors and that the optimization process of blasting parameters does not need to establish mathematical equations, which has the characteristics of selfadaptability, learning ability, fault tolerance, and robustness and can avoid the disadvantages of the traditional method for determining the blasting parameters of ore and rock [29]. Jhaveri designed the GA-BP network environment quality evaluation model structure.…”

Section: Related Workmentioning

confidence: 99%

Prediction and Analysis Method of Mine Blasting Quality Based on GA-BP Neural Network

Ren

2022

Mobile Information Systems

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Selecting reasonable blasting parameters of ore and rock is an important measure to achieve good blasting effect. In the mining process, rock fragmentation is an important index to evaluate the blasting effect, which directly affects the technical scheme, equipment selection, economic effect, and other issues of the mine and even seriously threatens the sustainable safety production of the mine. With the rapid development of information technology, the development of computer intelligent image recognition technology is becoming more and more perfect, and its role is becoming more and more important. Based on the neural network method, this paper studies the computer intelligent image recognition technology. In this paper, the GA-BP network image recognition model is established by combining genetic algorithm with BP algorithm and analyzing the principles of intelligent image recognition, image pattern recognition, and BP neural network learning algorithm. On the basis of experimental analysis, the average accuracy of prediction can reach 67.4%. For the efficiency analysis of computer mathematical analysis, it will generally reach 64.3%. In this paper, taking the lump rate and blasting cost as the optimization objective function, the comparison and selection of multiple schemes of production blasting design are carried out, which provides quantitative decision-making basis for the rational selection of production blasting design parameters.

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“…Clinical call support systems may assist patients to make better judgments than medical examiners [ 2 , 3 ]. In a world of cloud computing and fog computing, it is nearly impossible to extract healthcare demands without complete, comprehensive, and associated health data, and the security of this data is still another challenge [ 4 – 7 ]. Patient feedback is helpful in the pattern classification process, which establishes a patient's health status and degree of sickness.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques

Shehzadi

Hassan

Rizwan

et al. 2022

Computational Intelligence and Neuroscience

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Ischemic heart disease (IHD) causes discomfort or irritation in the chest. According to the World Health Organization, coronary heart disease is the major cause of mortality in Pakistan. Accurate model with the highest precision is necessary to avoid fatalities. Previously several models are tried with different attributes to enhance the detection accuracy but failed to do so. In this research study, an artificial approach to categorize the current stage of heart disease is carried out. Our model predicts a precise diagnosis of chronic diseases. The system is trained using a training dataset and then tested using a test dataset. Machine learning methods such as LR, NB, and RF are applied to forecast the development of a disease. Experimental outcomes of this research study have proven that our strategy has excelled other procedures with maximum accuracy of 99 percent for RF, 97 percent for NB, and 98 percent for LR. With such high accuracy, the number of deaths per year of ischemic heart disease will be slightly decreased.

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Application of Robust Zero-Watermarking Scheme Based on Federated Learning for Securing the Healthcare Data

Cited by 48 publications

References 31 publications

Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

Prediction and Analysis Method of Mine Blasting Quality Based on GA-BP Neural Network

Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques

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