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Federated Learning Approach to Protect Healthcare Data over Big Data Scenario
| Content Provider | MDPI |
|---|---|
| Author | Dhiman, Gaurav Juneja, Sapna Mohafez, Hamidreza El-Bayoumy, Ibrahim Sharma, Lokesh Kumar Hadizadeh, Maryam Islam, Mohammad Aminul Viriyasitavat, Wattana Khandaker, Mayeen Uddin |
| Copyright Year | 2022 |
| Description | The benefits and drawbacks of various technologies, as well as the scope of their application, are thoroughly discussed. The use of anonymity technology and differential privacy in data collection can aid in the prevention of attacks based on background knowledge gleaned from data integration and fusion. The majority of medical big data are stored on a cloud computing platform during the storage stage. To ensure the confidentiality and integrity of the information stored, encryption and auditing procedures are frequently used. Access control mechanisms are mostly used during the data sharing stage to regulate the objects that have access to the data. The privacy protection of medical and health big data is carried out under the supervision of machine learning during the data analysis stage. Finally, acceptable ideas are put forward from the management level as a result of the general privacy protection concerns that exist throughout the life cycle of medical big data throughout the industry. |
| Starting Page | 2500 |
| e-ISSN | 20711050 |
| DOI | 10.3390/su14052500 |
| Journal | Sustainability |
| Issue Number | 5 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-02-22 |
| Access Restriction | Open |
| Subject Keyword | Sustainability Information and Library Science Big Data Healthcare Mobile Device Patient Clinical Records Federated Learning |
| Content Type | Text |
| Resource Type | Article |
