Publishing locally private high-dimensional synthetic data efficiently

Abstract

The existing researches on local differential privacy mainly focus on low-dimensional data. These may be faced with high computation cost or low data accuracy when handling high-dimensional data. LoPub published high-dimensional crowd-sourced data, whose precision decreases with the increase of attributes pairs. In this paper, PrivPJ, a method to preserve local differential privacy of published high-dimensional data, is proposed, which can capture a tradeoff between the availability of results and privacy preservation. In PrivPJ, we design mVAE, a multivariate joint distribution algorithm that can alleviate the effect of increasing attributes pairs on accuracy. Then the complete graph is obtained based on the distribution and Markov network, and a local-differential-private junction tree is built based on the graph. PrivPJ can publish a locally private synthetic dataset with the approximate joint distribution. PrivPJ satisfies local differential privacy, and performs well on computation cost, estimation accuracy and misclassification rate of the algorithm on high-dimensional datasets.