Abstract
AbstractPersonalized medical data privacy and secure storage protection face serious challenges, especially in terms of data security and storage efficiency. Traditional encryption and storage solutions cannot meet the needs of modern medical data protection, which has led to an urgent need for new data protection strategies. Research personalized medical data privacy and secure storage protection based on hybrid encryption, in order to improve the security and efficiency of data storage. A hybrid encryption mechanism was proposed, which uses user attributes as keys for data encryption. The results show that the storage consumption of user attribute keys increases with the number of user attributes, but the consumption of hybrid encryption privacy storage technology is much smaller than that of traditional schemes. In the test, when the number of users increased to 30, the processing time first reached 1200 ms. During the increase in data volume, both test data and real data showed a brief decrease in attack frequency, but after the data volume reached 730–780, the attack frequency increased. It is worth noting that the performance of test data is better than that of real data. Personalized medical data privacy and secure storage protection based on hybrid encryption can not only effectively improve data security and reduce the risk of attack, but also greatly outperform traditional solutions in storage consumption and processing time. It has important practical significance for modern medical data storage protection.
Publisher
Springer Science and Business Media LLC
Reference27 articles.
1. W.P. Luo, C.S. Feng, L.P. Zou, Attribute-based encryption scheme with fast encryption. J. Softw. 31(12), 3923–3936 (2020)
2. Y.B. Miao, J.F. Ma, X.M. Liu, Attributed based keyword search over hierarchical data in cloud computing. IEEE Trans. Serv. Comput. 13(6), 985–998 (2020)
3. W. Li, C. Zhang, Privacy-aware sensing-quality based budget feasible incentive mechanism for crowdsourcing fingerprint collection. IEEE Access. 4, 49775–49784 (2020)
4. Y.R. Chen, H. Chen, M. Han, Security consensus algorithm of medical data based on credit rating. J. Electron Inform. Technol. 44(1), 279–287 (2022)
5. M. Gong, S. Wang, L. Wang, Evaluation of privacy risks of patients’ data in China: case study. JMIR Med. Inform. 8(2), e13046 (2020)
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