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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.