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  1. シンポジウム
  2. シンポジウムシリーズ
  3. コンピュータセキュリティシンポジウム
  4. 2023

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

https://ipsj.ixsq.nii.ac.jp/records/228803
https://ipsj.ixsq.nii.ac.jp/records/228803
de28951b-74f5-446f-ad9c-af40bb6756a1
名前 / ファイル ライセンス アクション
IPSJ-CSS2023190.pdf IPSJ-CSS2023190.pdf (1.2 MB)
 2025年10月23日からダウンロード可能です。
Copyright (c) 2023 by the Information Processing Society of Japan
非会員:¥660, IPSJ:学会員:¥330, CSEC:会員:¥0, SPT:会員:¥0, DLIB:会員:¥0
Item type Symposium(1)
公開日 2023-10-23
タイトル
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
言語 eng
キーワード
主題Scheme Other
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
School of Interdisciplinary Mathematical Sciences, Meiji University
著者所属(英)
en
School of Interdisciplinary Mathematical Sciences, Meiji University
著者名 Andres, Hernandez-Matamoros

× Andres, Hernandez-Matamoros

Andres, Hernandez-Matamoros

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Hiroaki, Kikuchi

× Hiroaki, Kikuchi

Hiroaki, Kikuchi

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著者名(英) Andres, Hernandez-Matamoros

× Andres, Hernandez-Matamoros

en Andres, Hernandez-Matamoros

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Hiroaki, Kikuchi

× Hiroaki, Kikuchi

en Hiroaki, Kikuchi

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論文抄録
内容記述タイプ Other
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
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
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