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A large amount of high-dimensional and heterogeneous data appear in practicalapplications, which are often published to third parties for data analysis,recommendations, targeted advertising, and reliable predictions. However,publishing these data may disclose personal sensitive information, resulting inan increasing concern on privacy violations. Privacy-preserving data publishinghas received considerable attention in recent years. Unfortunately, thedifferentially private publication of high dimensional data remains achallenging problem. In this paper, we propose a differentially privatehigh-dimensional data publication mechanism (DP2-Pub) that runs in two phases:a Markov-blanket-based attribute clustering phase and an invariant postrandomization (PRAM) phase. Specifically, splitting attributes into severallow-dimensional clusters with high intra-cluster cohesion and low inter-clustercoupling helps obtain a reasonable allocation of privacy budget, while adouble-perturbation mechanism satisfying local differential privacy facilitatesan invariant PRAM to ensure no loss of statistical information and thussignificantly preserves data utility. We also extend our DP2-Pub mechanism tothe scenario with a semi-honest server which satisfies local differentialprivacy. We conduct extensive experiments on four real-world datasets and theexperimental results demonstrate that our mechanism can significantly improvethe data utility of the published data while satisfying differential privacy.

DP2-Pub: Differentially Private High-Dimensional Data Publication with Invariant Post Randomization