Efficient privacy-preserving classification construction model with differential privacy technology

To address the problem of privacy disclosure during data mining, a new privacy-preserving decision tree classification construction model based on a differential privacy-protection mechanism is presented. An efficient classifier that uses feedback to add two types of noise via Laplace and exponential mechanisms to perturb the calculation results are introduced to the construction algorithm that provides a secure data access interface for users. Different split solutions for attributes of continuous and discrete values are provided and used to optimize the search scheme to reduce the error rate of the classifier. By choosing an available quality function with lower sensitivity for making decisions and improving the privacy budget allocation methods, the algorithm effectively resists malicious attacks that depend on the background knowledge. The potential problem of obtaining personal information by guessing unknown sensitive nodes of tree-type data is solved correspondingly. The better privacy preservation and accuracy of this new algorithm are shown by simulation experiments.