P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model

• URL: http://arxiv.org/abs/2006.12101v4
• Date: Mon, 7 Mar 2022 11:56:09 GMT
• Title: P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model
• Authors: Shun Takagi, Tsubasa Takahashi, Yang Cao, Masatoshi Yoshikawa
• Abstract summary: This paper proposes privacy-preserving phased generative model (P3GM) for releasing sensitive data. P3GM employs the two-phase learning process to make it robust against the noise, and to increase learning efficiency. 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.
• Score: 23.91327154831855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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