  How can we release a massive volume of sensitive data while mitigating
privacy risks? Privacy-preserving data synthesis enables the data holder to
outsource analytical tasks to an untrusted third party. The state-of-the-art
approach for this problem is to build a generative model under differential
privacy, which offers a rigorous privacy guarantee. However, the existing
method cannot adequately handle high dimensional data. In particular, when the
input dataset contains a large number of features, the existing techniques
require injecting a prohibitive amount of noise to satisfy differential
privacy, which results in the outsourced data analysis meaningless. To address
the above issue, this paper proposes privacy-preserving phased generative model
(P3GM), which is a differentially private generative model for releasing such
sensitive data. P3GM employs the two-phase learning process to make it robust
against the noise, and to increase learning efficiency (e.g., easy to
converge). We give theoretical analyses about the learning complexity and
privacy loss in P3GM. We further experimentally evaluate our proposed method
and demonstrate that P3GM significantly outperforms existing solutions.
Compared with the state-of-the-art methods, our generated samples look fewer
noises and closer to the original data in terms of data diversity. Besides, in
several data mining tasks with synthesized data, our model outperforms the
competitors in terms of accuracy.
