Huang Yonggang, Teng Teng, Li Yuanyuan, Zhang Minghao
Information Management, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China.
PLoS One. 2025 Mar 27;20(3):e0317119. doi: 10.1371/journal.pone.0317119. eCollection 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%.
当前的数据访问控制方法主要利用区块链技术。然而，在处理高维医学数据时，区块链固有的透明度与保护患者隐私的必要性相冲突。因此，这增加了敏感信息暴露的风险。为了增强患者隐私，采用了一种模糊加密算法。这可以防止对敏感医学数据的未经授权访问和解密。因此，提出了一种基于模糊算法的高维医学数据属性加密访问控制方法。利用相位数据和频率数据来评估医学数据属性的稳定性。此外，应用经验模态分解方法来消除这些属性中的噪声。使用模糊加密算法的密钥配置，对同一字段内不同安全级别的高维医学数据属性进行加密和解密处理。此外，计算对这些数据属性的访问行为的信任度以维护安全性。医学用户成功登录后，分析其访问权限以有效控制高维医学用户的加密访问权限。建立访问请求图以有效控制对高维医学数据属性的加密访问。实验结果表明，当数据属性数量达到数百万时，加密访问控制时间仍小于60毫秒。最大加密时间减少了21毫秒，并且在应用过程中的抗攻击成功率很高。从最大成功率的比较可以看出，该方法在抗攻击方面的成功率提高了8.5%。
