A Global Medical Data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers

2024-10-04 · Vinaytosh Mishra, Kishu Gupta, Deepika Saxena, Ashutosh Kumar Singh

Electronic Health Records (EHR) are crucial for the success of digital healthcare, with a focus on putting consumers at the center of this transformation. However, the digitalization of healthcare records brings along security and privacy risks for personal data. The major concern is that different countries have varying standards for the security and privacy of medical data. This paper proposed a novel and comprehensive framework to standardize these rules globally, bringing them together on a common platform. To support this proposal, the study reviews existing literature to understand the research interest in this issue. It also examines six key laws and standards related to security and privacy, identifying twenty concepts. The proposed framework utilized K-means clustering to categorize these concepts and identify five key factors. Finally, an Ordinal Priority Approach is applied to determine the preferred implementation of these factors in the context of EHRs. The proposed study provides a descriptive then prescriptive framework for the implementation of privacy and security in the context of electronic health records. Therefore, the findings of the proposed framework are useful for professionals and policymakers in improving the security and privacy associated with EHRs.

📄 PDF Abstract BibTeX arXiv:2410.03621

Code (0)

등록된 구현이 없습니다.

Tasks

DescriptivePrivacy Preserving

Methods 이 논문이 사용한 방법론

Focus 설명 없음

k-Means Clustering k-Means Clustering is a clustering algorithm that divides a training set into $k$ different clusters of examples that are near each other. It works by initializing $k$…

BFV-Based Homomorphic Encryption for Privacy-Preserving CNN Models

2022-07-01 · journal 2022 7 · FebriantiWibawa, Ferhat Ozgur Catak, Salih Sarp, Murat Kuzlu

Medical data is frequently quite sensitive in terms of data privacy and security. Federated learning has been used to increase the privacy and security of medical data, which is a sort of machine learning technique. Th…

Federated Learning Privacy Preserving

ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification

2024-11-08 · Al Amin, Kamrul Hasan, Sharif Ullah, M. Shamim Hossain

Privacy-preserving and secure data sharing are critical for medical image analysis while maintaining accuracy and minimizing computational overhead are also crucial. Applying existing deep neural networks (DNNs) to encry…

Medical Image Analysis Privacy Preserving

Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case

2022-04-16 · Febrianti Wibawa, Ferhat Ozgur Catak, Salih Sarp, Murat Kuzlu 외

Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the improvement of the privacy and security …

Adversarial Attack Federated Learning Privacy Preserving

Privacy-preserving medical image analysis

2020-12-10 · Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin 외

The utilisation of artificial intelligence in medicine and healthcare has led to successful clinical applications in several domains. The conflict between data usage and privacy protection requirements in such systems mu…

Federated Learning Medical Image Analysis Privacy Preserving

Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation

2023-08-01 · Muhammad Irfan Khan, Esa Alhoniemi, Elina Kontio, Suleiman A. Khan 외

Federated Learning (FL) is a distributed machine learning approach that safeguards privacy by creating an impartial global model while respecting the privacy of individual client data. However, the conventional FL method…

Benchmarking Brain Tumor Segmentation Federated Learning Image Segmentation+4