医学教育大数据隐私保护治理OA

Objective To explore privacy protection technologies and strategies for medical education big data in the process of data collection,storage,and sharing,in order to improve data uti-lization and explore its educational value.Methods Analyze the sources and privacy protection chal-lenges of medical education big data,propose anonymous technology and differential privacy tech-nology for the data collection stage,encryption technology and audit mechanism for the data stor-age stage,and federated learning technology and blockchain technology for the data sharing stage.Results Each stage of technology addresses specific privacy risks through targeted design:the k-anonymity model reduces the recognition probability of a single record to 1/k by generalizing quasi identifiers,which can reduce identity inference risks by more than 70.0%;The symmetric encryption algorithm achieves an encryption efficiency of 100MB/s for stored data,and when combined with an audit mechanism,it can increase the data tampering detection rate to 99.8%;Federated learning achieves zero leakage of raw data through distributed model training in collaborative scientific research across multiple universities;Blockchain technology utilizes tamper proof features to achieve secure sharing of cross institutional data.Meanwhile,management strategies provide institutional guarantees for the implementation of technology.Conclusion The combination of technical protection and man-agement strategies can effectively enhance the privacy protection level of medical education big data,promote its maximum utilization in a safe and reliable environment,and provide strong support for the medical education industry.