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Geometry-Aware Tabular Diffusion
David Turtora Zagardo
Tabular synthesis is critical for privacy-preserving sharing and augmentation, yet diffusion models rely on implicit mechanisms to capture inter-column relationships. We introduce…
Differentially Private Block-wise Gradient Shuffle for Deep Learning
David Zagardo
Traditional Differentially Private Stochastic Gradient Descent (DP-SGD) introduces statistical noise on top of gradients drawn from a Gaussian distribution to ensure privacy. This…
A More Practical Approach to Machine Unlearning
David Zagardo
Machine learning models often incorporate vast amounts of data, raising significant privacy concerns. Machine unlearning, the ability to remove the influence of specific data point…
Too Good to be True? Turn Any Model Differentially Private With DP-Weights
David Zagardo
Imagine training a machine learning model with Differentially Private Stochastic Gradient Descent (DP-SGD), only to discover post-training that the noise level was either too high,…