基于属性分类的分布式大数据隐私保护加密控制模型设计

在分布式大数据的存储和传输过程中,数据极易被恶意用户攻击,造成数据的泄露和丢失。为提高分布式大数据的存储和传输安全性,设计了基于属性分类的分布式大数据隐私保护加密控制模型。挖掘用户隐私数据,以分布式结构存储。根据分布式隐私数据特征,判断数据的属性类型。利用Logistic混沌映射,迭代生成数据隐私保护密钥,通过匿名化、混沌映射、同态加密等步骤,实现对隐私数据的加密处理。利用属性分类技术,控制隐私保护数据访问进程,在传输协议的约束下,实现分布式大数据隐私保护加密控制。实验结果表明,设计模型的明文和密文相似度较低,访问撤销控制准确率高达98.9%,在有、无攻击工况下,隐私数据损失量较少,具有较好的加密、控制性能和隐私保护效果,有效降低了隐私数据的泄露风险,提高了分布式大数据的存储和传输安全性。

Design of distributed big data privacy protection encryption control model based on attribute classification

In the storage and transmission process of distributed big data, data is highly susceptible to malicious user attacks, resulting in data leakage and loss. To improve the storage and transmission security of distributed big data, a distributed big data privacy protection encryption control model based on attribute classification was designed. Mining user privacy data and storing it in a distributed structure. Determine the attribute type of the distributed privacy data based on its characteristics. Using Logistic chaotic mapping, the data privacy protection key is generated iteratively, and the encryption of private data is realized through anonymization, chaotic mapping, homomorphic encryption and other steps. Utilize attribute classification technology to control the access process of privacy protection data, and achieve distributed big data privacy protection encryption control under the constraints of transmission protocols. The experimental results show that the similarity between plaintext and ciphertext in the designed model is low, and the accuracy of access revocation control is as high as 98.9%. Under both attack and no attack conditions, the loss of privacy data is relatively small, and it has good encryption, control performance, and privacy protection effects. It effectively reduces the risk of privacy data leakage and improves the storage and transmission security of distributed big data.