arXiv:2602. 05833v2 Announce Type: replace Abstract: There is a need for synthetic training and test datasets that replicate statistical distributions of original datasets without compromising their confidentiality.
By Laura Plein, Alexi Turcotte, Arina Hallemans, Andreas Zeller
arXiv:2608. 10891v1 Announce Type: new Abstract: Time series forecasting in privacy-sensitive domains often requires training models on released data rather than original observations.
By Luis Amorim, Vitor Cerqueira, Moises Santos, Paulo J. Azevedo, Carlos Soares
arXiv:2606. 08259v1 Announce Type: new Abstract: This paper investigates the problem of generating synthetic tabular data with differential privacy (DP) guarantees, enabling data sharing in sensitive domains.
By Toan Tran, Arturs Backurs, Zinan Lin, Victor Reis, Li Xiong, Sergey Yekhanin
arXiv:2606. 07640v1 Announce Type: cross Abstract: This study investigates the trade-offs between fidelity, privacy, and utility in synthetic data generation under conditions of data scarcity and privacy sensitivity.
By Borja Arroyo Galende, Alejandro Almod\'ovar, Patricia A. Apell\'aniz, Juan Parras, Silvia Uribe, Santiago Zazo
arXiv:2507. 19700v2 Announce Type: replace Abstract: We propose a new framework for generating tabular synthetic datasets via disjoint generative models.
By Anton Danholt Lautrup, Muhammad Rajabinasab, Tobias Hyrup, Arthur Zimek, Peter Schneider-Kamp
arXiv:2405. 16361v4 Announce Type: replace Abstract: To protect privacy in regulated domains such as healthcare and finance, model owners may allow only remote API access while keeping both the training data and model parameters private.
By Kexin Li, Aastha Mehta, David Lie