Big data applications provide convenience for people's life and work style

but in the process of data publishing

personal privacy information

such as consumption records

social relations

and so on

are collected by service providers all the time

and users' privacy is threatened greatly. Aiming at the significant relief of data processing pressure on single server

we propose a multi-cluster distributed differential privacy data publishing method based on neural network (MCDP)

which effectively improves the prediction accuracy and efficiency

and different privacy parameters between clusters guarantee the flexibility of the protocol. Especially

because the central server stores statistical data after differential privacy processing

it does not collect individual privacy data

even if the central server is attacked

the user data will not be leaked. Experiments show that MCDP has obvious advantages in privacy processing efficiency

privacy protection intensity

prediction accuracy and prediction efficiency.