SynC2S: An Efficient Method for Synthesizing Tabular Data With a Learnable Pre-Processing
There has been a growing demand to access large public datasets to extract valuable insights or enhance their services. However, this also involves risks, such as privacy breaches and unauthorized data exposure. Data synthesis has emerged as a popular technique to address privacy preservation and da...
Saved in:
| Main Authors: | Jiwoo Kim, Seri Park, Junsung Koh, Dongha Kim |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10704657/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Variational Autoencoding with Conditional Iterative Sampling for Missing Data Imputation
by: Shenfen Kuang, et al.
Published: (2024-10-01) -
Comprehensive evaluation framework for synthetic tabular data in health: fidelity, utility and privacy analysis of generative models with and without privacy guarantees
by: Mikel Hernandez, et al.
Published: (2025-04-01) -
Enhancing RAG Performance by Representing Hierarchical Nodes in Headers for Tabular Data
by: Minchae Song
Published: (2025-01-01) -
Implementasi Sistem Pendukung Keputusan Pengadaan Mobil Menggunakan Metode Simple Additive Weighting Pada Aplikasi Rental Mobil
by: Ritriandrey Londong Allo, et al.
Published: (2020-11-01) -
Robust Synthetic Data Generation for Sequential Financial Models Using Hybrid Variational Autoencoder–Markov Chain Monte Carlo Architectures
by: Francesco Bruni Prenestino, et al.
Published: (2025-02-01)