Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Protecting Personal Healthcare Record Using Blockchain & Federated Learning Technologies
47
Zitationen
7
Autoren
2022
Jahr
Abstract
For decades artificial intelligence (AI) has been used for various applications in the healthcare industry. Machine learning and artificial intelligence algorithms allow us to diagnose and customize medical care and follow-up plans to get better results, and during the covid19 pandemic, it was found that AI models have been using to predict the Covid-19 symptoms, understanding how it spreads, speeding up research and treatment using medical data. However, it is very challenging to make a robust AI model and use it in a real-time and real-world environment since most organizations do not want to share their data with other third parties due to privacy concerns, furthermore, it is difficult to build a generalized prediction model because of the fragmented nature of the patient data across the healthcare system. To solve the above problems, this paper presents a solution based on blockchain and AI technologies. The blockchain will securely protect the data access and AI-based federated learning for building a robust model for global and real-time usage.
Ähnliche Arbeiten
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
2002 · 8.396 Zit.
Calibrating Noise to Sensitivity in Private Data Analysis
2006 · 6.872 Zit.
Deep Learning with Differential Privacy
2016 · 5.595 Zit.
Communication-Efficient Learning of Deep Networks from Decentralized\n Data
2016 · 5.591 Zit.
Large-Scale Machine Learning with Stochastic Gradient Descent
2010 · 5.564 Zit.