On Rényi Differential Privacy in Statistics-based Synthetic Data Generation

• Item type: Journal(1)
• 公開日: 2023-12-15
• タイトル: On Rényi Differential Privacy in Statistics-based Synthetic Data Generation
• タイトル / 言語: en
• タイトル: On Rényi Differential Privacy in Statistics-based Synthetic Data Generation
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集:次世代デジタルプラットフォームにおける情報流通を支えるセキュリティとトラスト（推薦論文）] synthetic data generation, Rényi differential privacy, privacy protection
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: NTT Social Informatics Laboratories
• 著者所属: NTT Social Informatics Laboratories
• 著者所属: NTT Social Informatics Laboratories
• 著者所属: NTT Social Informatics Laboratories
• 著者所属: NTT Social Informatics Laboratories
• 著者所属: Faculty of Informatics, Gunma University
• 著者所属(英): en
• 著者所属(英): NTT Social Informatics Laboratories
• 著者所属(英): en
• 著者所属(英): NTT Social Informatics Laboratories
• 著者所属(英): en
• 著者所属(英): NTT Social Informatics Laboratories
• 著者所属(英): en
• 著者所属(英): NTT Social Informatics Laboratories
• 著者所属(英): en
• 著者所属(英): NTT Social Informatics Laboratories
• 著者所属(英): en
• 著者所属(英): Faculty of Informatics, Gunma University
• 著者名: Takayuki, Miura; Toshiki, Shibahara; Masanobu, Kii; Atsunori, Ichikawa; Juko, Yamamoto; Koji, Chida
• 著者名(英): Takayuki, Miura; Toshiki, Shibahara; Masanobu, Kii; Atsunori, Ichikawa; Juko, Yamamoto; Koji, Chida
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Privacy protection with synthetic data generation often uses differentially private statistics and model parameters to quantitatively express theoretical security. However, these methods do not take into account privacy protection due to the randomness of data generation. In this paper, we theoretically evaluate Rényi differential privacy of the randomness in data generation of a synthetic data generation method that uses the mean vector and the covariance matrix of an original dataset. Specifically, for a fixed α > 1, we show the condition of ε such that the synthetic data generation satisfies (α, ε)-Rényi differential privacy under a bounded neighboring condition and an unbounded neighboring condition, respectively. In particular, under the unbounded condition, when the size of the original dataset and synthetic dataset is 10 million, the mechanism satisfies (4, 0.576)-Rényi differential privacy. We also show that when we translate it into the traditional (ε, δ)-differential privacy, the mechanism satisfies (4.46, 10-14)-differential privacy.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.31(2023) (online); DOI http://dx.doi.org/10.2197/ipsjjip.31.812; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Privacy protection with synthetic data generation often uses differentially private statistics and model parameters to quantitatively express theoretical security. However, these methods do not take into account privacy protection due to the randomness of data generation. In this paper, we theoretically evaluate Rényi differential privacy of the randomness in data generation of a synthetic data generation method that uses the mean vector and the covariance matrix of an original dataset. Specifically, for a fixed α > 1, we show the condition of ε such that the synthetic data generation satisfies (α, ε)-Rényi differential privacy under a bounded neighboring condition and an unbounded neighboring condition, respectively. In particular, under the unbounded condition, when the size of the original dataset and synthetic dataset is 10 million, the mechanism satisfies (4, 0.576)-Rényi differential privacy. We also show that when we translate it into the traditional (ε, δ)-differential privacy, the mechanism satisfies (4.46, 10-14)-differential privacy.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.31(2023) (online); DOI http://dx.doi.org/10.2197/ipsjjip.31.812; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 64, 号 12, 発行日 2023-12-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764
• 公開者 / 言語: ja
• 公開者 / 出版者: 情報処理学会