Next Article in Journal
Advanced Manifold–Metric Pairs
Previous Article in Journal
Line Defects in Two-Dimensional Dodecagonal Quasicrystals
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Privacy-Aware Table Data Generation by Adversarial Gradient Boosting Decision Tree

1
Graduate School of Advanced Science and Engineering, Hiroshima University, Kagamiyama 1-7-1, Higashi-Hiroshima 739-8521, Japan
2
Department of Computer Science and Engineering, Khulna University of Engineering and Technology, Khulna 9203, Bangladesh
*
Author to whom correspondence should be addressed.
Mathematics 2025, 13(15), 2509; https://doi.org/10.3390/math13152509
Submission received: 7 July 2025 / Revised: 23 July 2025 / Accepted: 30 July 2025 / Published: 4 August 2025

Abstract

Privacy preservation poses significant challenges in third-party data sharing, particularly when handling table data containing personal information such as demographic and behavioral records. Synthetic table data generation has emerged as a promising solution to enable data analysis while mitigating privacy risks. While Generative Adversarial Networks (GANs) are widely used for this purpose, they exhibit limitations in modeling table data due to challenges in handling mixed data types (numerical/categorical), non-Gaussian distributions, and imbalanced variables. To address these limitations, this study proposes a novel adversarial learning framework integrating gradient boosting trees for synthesizing table data, called Adversarial Gradient Boosting Decision Tree (AGBDT). Experimental evaluations on several datasets demonstrate that our method outperforms representative baseline models regarding statistical similarity and machine learning utility. Furthermore, we introduce a privacy-aware adaptation of the framework by incorporating k-anonymization constraints, effectively reducing overfitting to source data while maintaining practical usability. The results validate the balance between data utility and privacy preservation achieved by our approach.
Keywords: adversarial learning; decision trees; tree ensembles; privacy evaluation adversarial learning; decision trees; tree ensembles; privacy evaluation

Share and Cite

MDPI and ACS Style

Jiang, S.; Iwata, N.; Kamei, S.; Alam, K.M.R.; Morimoto, Y. Privacy-Aware Table Data Generation by Adversarial Gradient Boosting Decision Tree. Mathematics 2025, 13, 2509. https://doi.org/10.3390/math13152509

AMA Style

Jiang S, Iwata N, Kamei S, Alam KMR, Morimoto Y. Privacy-Aware Table Data Generation by Adversarial Gradient Boosting Decision Tree. Mathematics. 2025; 13(15):2509. https://doi.org/10.3390/math13152509

Chicago/Turabian Style

Jiang, Shuai, Naoto Iwata, Sayaka Kamei, Kazi Md. Rokibul Alam, and Yasuhiko Morimoto. 2025. "Privacy-Aware Table Data Generation by Adversarial Gradient Boosting Decision Tree" Mathematics 13, no. 15: 2509. https://doi.org/10.3390/math13152509

APA Style

Jiang, S., Iwata, N., Kamei, S., Alam, K. M. R., & Morimoto, Y. (2025). Privacy-Aware Table Data Generation by Adversarial Gradient Boosting Decision Tree. Mathematics, 13(15), 2509. https://doi.org/10.3390/math13152509

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop