| Title |
A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology |
| ID_Doc |
43320 |
| Authors |
Singh, S; Rathore, S; Alfarraj, O; Tolba, A; Yoon, B |
| Title |
A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology |
| Year |
2022 |
| Published |
|
| DOI |
10.1016/j.future.2021.11.028 |
| Abstract |
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. (c) 2021 Published by Elsevier B.V. |
| Author Keywords |
Federated Learning; Privacy-preserving; Blockchain; Internet-of-Things |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000770661300007 |
| WoS Category |
Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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