  A large amount of high-dimensional and heterogeneous data appear in practical
applications, 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 in
an increasing concern on privacy violations. Privacy-preserving data publishing
has received considerable attention in recent years. Unfortunately, the
differentially private publication of high dimensional data remains a
challenging problem. In this paper, we propose a differentially private
high-dimensional data publication mechanism (DP2-Pub) that runs in two phases:
a Markov-blanket-based attribute clustering phase and an invariant post
randomization (PRAM) phase. Specifically, splitting attributes into several
low-dimensional clusters with high intra-cluster cohesion and low inter-cluster
coupling helps obtain a reasonable allocation of privacy budget, while a
double-perturbation mechanism satisfying local differential privacy facilitates
an invariant PRAM to ensure no loss of statistical information and thus
significantly preserves data utility. We also extend our DP2-Pub mechanism to
the scenario with a semi-honest server which satisfies local differential
privacy. We conduct extensive experiments on four real-world datasets and the
experimental results demonstrate that our mechanism can significantly improve
the data utility of the published data while satisfying differential privacy.
