Attribute encryption access control method of high dimensional medical data based on fuzzy algorithm - Yonggang Huang; Teng Teng; Yuanyuan Li; Minghao Zhang (PLoS ONE, 2025)
The current approach to data access control predominantly utilizes blockchain technology. However, when dealing with high-dimensional medical data, the inherent transparency of blockchain conflicts with the necessity of protecting patient privacy. Consequently, this increases the risk of sensitive information exposure. To enhance patient privacy, a fuzzy encryption algorithm is employed. This prevents unauthorized access and decryption of sensitive medical data. Consequently, a high-dimensional medical data attribute encryption access control method based on fuzzy algorithm is proposed. Phase data and frequency data are utilized to assess the stability of medical data attributes. Additionally, the empirical mode decomposition method is applied to eliminate noise from these attributes. Using the key configuration of fuzzy encryption algorithm, high-dimensional medical data attributes with different security levels within the same field undergo encryption and decryption processes. Moreover, the trust degree of access behavior towards these data attributes is calculated to maintain security. After the medical users successfully log in, their access permissions are analyzed to effectively control the encrypted access permissions of high-dimensional medical users. The access request graph is established to effectively control encrypted access to high-dimensional medical data attributes. The experimental results showed that when the number of data attributes reached millions, the encryption access control time was still less than 60ms. The maximum encryption time was reduced by 21ms, and the anti-attack success rate was high during the application process. From the comparison of the maximum success rates, it can be seen that the success rate of this method in resisting attacks has increased by 8.5%.