A comparison of synthetic data approaches using utility and disclosure risk measures-Reference-Cited by-同舟云学术

A comparison of synthetic data approaches using utility and disclosure risk measures

Published:2023-04-30 Issue:2 Volume:36 Page:141-166
ISSN:1225-066X
Container-title:Korean Journal of Applied Statistics
language:en
Short-container-title:KJAS

Author:

An Seongbin¹,Doan Trang²,Lee Juhee³,Kim Jiwoo⁴,Kim Yong Jae⁵,Kim Yunji¹,Yoon Changwon¹,Jung Sungkyu⁵,Kim Dongha⁴,Kwon Sunghoon²,Kim Hang J⁶,Ahn Jeongyoun¹,Park Cheolwoo⁷

Affiliation:

1. Department of Industrial & Systems Engineering, KAIST

2. Department of Applied Statistics, Konkuk University

3. Department of Statistics, Kyungpook National University

4. Department of Statistics, Sungshin Women’s University

5. Department of Statistics, Seoul National University

6. Division of Statistics and Data Science, University of Cincinnati

7. Department of Mathematical Sciences, KAIST

Publisher

The Korean Statistical Society

Subject

General Earth and Planetary Sciences,General Engineering,General Environmental Science

Link

http://pdf.medrang.co.kr/KJAS/2023/036/KJAS036-02-141.pdf

Reference40 articles.

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5. Dhariwal P and Nichol A (2021). Diffusion models beat gans on image synthesis, Advances in Neural Information Processing Systems, 34, 8780-8794.