arXiv:2606.13105v2 Announce Type: replace
Abstract: We revisit the fairness notion of disparate impact for synthetic data generation (SDG), that assesses whether the utility of generated records is t...
By Paul Andrey, Micha\"el Perrot, Batiste Le Bars, Marc Tommasi
arXiv:2607. 07471v1 Announce Type: cross Abstract: Machine learning models are increasingly deployed in high-stakes domains, raising concerns about both privacy and fairness.
By Vin\'icius Gabriel Angelozzi, H\'eber H. Arcolezi
Machine learning models are increasingly deployed in high-stakes domains, raising concerns about both privacy and fairness. Differential Privacy (DP) has become a gold standard for privacy-preserving data analysis, while fairness-aware mechanisms aim to mitigate discrimination against underrepresented groups.
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:2604. 07486v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have emerged as a powerful tool for synthetic data generation.
By Qian Ma, Sarah Rajtmajer
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