Aiming at the privacy protection problem in the current data release, the classical K-Anonymity model and the improved L-Diversity model are analyzed. Combining the advantages of the two models, an enhanced privacy protection model is proposed and the algorithm is implemented. The new model enhances the validity of data distribution by introducing clustering method. At the same time in the clustering process using a new information loss measurement standards to enhance the security and flexibility of data release. The experimental results show that the model can reduce the risk of privacy leakage, and has a small loss of information.
The traditional centralized storage of traditional electronic medical records (EMRs) faces problems like data leakage, data loss, and EMR misplacement. The current protection measures for patients’ privacy in EMRs cannot withstand the fast-developing password cracking technologies and frequency cyberattacks. This paper intends to innovate the information sharing and privacy protection of electronic nursing records (ENRs) management system. Specifically, the signature interception technology was introduced to EMRs, the different phases of certificateless signature interception scheme were depicted, and the validation procedures of the scheme were designed. Then, the six phases of ENR information sharing protocol based on alliance blockchain were described in detail. Finally, an end-to-end memory neural network was constructed for ENR classification. The proposed management scheme was proved effective through experiments.
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