Federated Learning Approach to Protect Healthcare Data over Big Data Scenario

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.

Automated summary

This article thoroughly discusses the benefits and drawbacks of various technologies and their scope of application. By utilizing anonymity technology and differential privacy in data collection, attacks based on background knowledge can be prevented. Encryption, auditing procedures, and access control mechanisms are used to ensure the confidentiality and integrity of medical big data during storage and sharing stages. Machine learning is employed for privacy protection during the data analysis stage. Acceptable ideas are proposed from the management level to address privacy concerns throughout the life cycle of medical big data.