A Randomized Response Layer for Ensuring User Privacy in Synthetic Data Generation

• Item type: Symposium(1)
• 公開日: 2023-10-23
• タイトル: A Randomized Response Layer for Ensuring User Privacy in Synthetic Data Generation
• タイトル / 言語: en
• タイトル: A Randomized Response Layer for Ensuring User Privacy in Synthetic Data Generation
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Synthetic Data, Randomize Response
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_5794
• 資源タイプ: conference paper
• 著者所属: School of Interdisciplinary Mathematical Sciences, Meiji University
• 著者所属: School of Interdisciplinary Mathematical Sciences, Meiji University
• 著者所属(英): en
• 著者所属(英): School of Interdisciplinary Mathematical Sciences, Meiji University
• 著者所属(英): en
• 著者所属(英): School of Interdisciplinary Mathematical Sciences, Meiji University
• 著者名: Andres, Hernandez-Matamoros; Hiroaki, Kikuchi
• 著者名(英): Andres, Hernandez-Matamoros; Hiroaki, Kikuchi
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: This paper introduces a modification to the Variational Auto Encoder by incorporating a randomized response (RR) layer before feeding the raw data into the encoder. This modification allows us to create a synthetic dataset that ensures user privacy. We generate synthetic data from four open datasets and compare it with well-known synthetic approaches. Our evaluation focuses on computing utility using the average variant distance, which quantifies the disparity between the real and synthetic data in terms of joint distributions on real and synthetic datasets. Additionally, for evaluating privacy, we measure each row in the synthetic data is novel using a tool developed by SDV. Our proposed method demonstrates similar AVD values compared to the no-privacy methods and higher AVD values compared to privacy method.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: This paper introduces a modification to the Variational Auto Encoder by incorporating a randomized response (RR) layer before feeding the raw data into the encoder. This modification allows us to create a synthetic dataset that ensures user privacy. We generate synthetic data from four open datasets and compare it with well-known synthetic approaches. Our evaluation focuses on computing utility using the average variant distance, which quantifies the disparity between the real and synthetic data in terms of joint distributions on real and synthetic datasets. Additionally, for evaluating privacy, we measure each row in the synthetic data is novel using a tool developed by SDV. Our proposed method demonstrates similar AVD values compared to the no-privacy methods and higher AVD values compared to privacy method.
• 書誌情報: コンピュータセキュリティシンポジウム2023論文集; p. 1397-1404, 発行日 2023-10-23
• 出版者 / 言語: ja
• 出版者: 情報処理学会