Abstract: With the increasing availability of high-dimensional data collected from numerous users,preserving user privacy while utilizing high-dimensional data poses significant challenges.This paper focuses on the problem of high-dimensional data publication under local differential privacy.State-of-the-art solutions first construct probabilistic graphical models to generate a set of noisy low-dimensional marginal distributions of the input data,and then use them to approximate the joint distribution of the input dataset for generating synthetic datasets.However,existing methods have limitations in computing marginal distributions for a large number of attribute pairs to construct probabilistic graphical models,as well as in calculating joint distributions for attribute subsets within the probabilistic graphical models.To address these limitations,this paper proposes a method PrivHDP(high-dimensional data publication under local differential privacy) for high-dimensional data publication under local differential privacy.Firstly,it uses random sampling response instead of the traditional privacy budget splitting strategy to perturb user data.It proposes an adaptive marginal distribution computation method to compute the marginal distributions of pairwise attributes and construct a Markov network.Secondly,it employs a novel method to measure the correlation between pairwise attributes,replacing mutual information.This method introduces a threshold technique based on high-pass filtering to reduce the search space during the construction of the probabilistic graphical model.It combines sufficient triangulation operations and a joint tree algorithm to obtain a set of attribute subsets.Finally,based on joint distribution decomposition and redundancy elimination,the proposed method computes the joint distribution over attribute subsets.Experimental results on four real datasets demonstrate that the PrivHDP algorithm outperforms similar algorithms in terms of k-way query and SVM classification accuracy,validating its effectiveness and efficiency.

Key words: Local differential privacy, High-dimensional data, Data publication, Marginal distribution, Joint distribution