This study compares the TopDown algorithm and private synthetic data generation methods in terms of differential privacy. Differential privacy is used to protect hierarchical, tabular population data, such as census…
Continue readingThis article introduces a large-scale dataset for 6-DoF localization of Unmanned Aerial Vehicles (UAVs) called UAVD4L. The dataset includes a 3D reference textured model which allows the generation of synthetic…
Continue readingThe article discusses the significance of synthetic data in the field of artificial intelligence. It emphasizes the challenges and potential biases these datasets may harbor and explores various methods of…
Continue readingThis academic article by researchers from the University of Waterloo focuses on the challenge of generating synthetic data with differential privacy (DP) guarantees when the input data has missing values….
Continue readingThis paper introduces a new dataset condensation framework called Squeeze, Recover, and Relabel (SRe2L). The framework decouples the bilevel optimization of model and synthetic data during training, allowing it to…
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