TY - JOUR
T1 - A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology
AU - Singh, Saurabh
AU - Rathore, Shailendra
AU - Alfarraj, Osama
AU - Tolba, Amr
AU - Yoon, Byungun
N1 - Funding Information: This work was supported in part by the National Research Foundation of Korea under Grant 2019R1A2C1085388. This work was funded by the Researchers Support . Publisher Copyright:© 2021
PY - 2022/4/1
Y1 - 2022/4/1
N2 - With the dramatically increasing deployment of IoT (Internet-of-Things) and communication, data has always been a major priority to achieve intelligent healthcare in a smart city. For the modern environment, valuable assets are user IoT data. The privacy policy is even the biggest necessity to secure user's data in a deep-rooted fundamental infrastructure of network and advanced applications, including smart healthcare. Federated learning acts as a special machine learning technique for privacy-preserving and offers to contextualize data in a smart city. This article proposes Blockchain and Federated Learning-enabled Secure Architecture for Privacy-Preserving in Smart Healthcare, where Blockchain-based IoT cloud platforms are used for security and privacy. Federated Learning technology is adopted for scalable machine learning applications like healthcare. Furthermore, users can obtain a well-trained machine learning model without sending personal data to the cloud. Moreover, it also discussed the applications of federated learning for a distributed secure environment in a smart city.
AB - With the dramatically increasing deployment of IoT (Internet-of-Things) and communication, data has always been a major priority to achieve intelligent healthcare in a smart city. For the modern environment, valuable assets are user IoT data. The privacy policy is even the biggest necessity to secure user's data in a deep-rooted fundamental infrastructure of network and advanced applications, including smart healthcare. Federated learning acts as a special machine learning technique for privacy-preserving and offers to contextualize data in a smart city. This article proposes Blockchain and Federated Learning-enabled Secure Architecture for Privacy-Preserving in Smart Healthcare, where Blockchain-based IoT cloud platforms are used for security and privacy. Federated Learning technology is adopted for scalable machine learning applications like healthcare. Furthermore, users can obtain a well-trained machine learning model without sending personal data to the cloud. Moreover, it also discussed the applications of federated learning for a distributed secure environment in a smart city.
U2 - 10.1016/j.future.2021.11.028
DO - 10.1016/j.future.2021.11.028
M3 - Article
AN - SCOPUS:85121920313
VL - 129
SP - 380
EP - 388
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
SN - 0167-739X
ER -