[2603.24695] Amplified Patch-Level Differential Privacy for Free via Random Cropping

Computer Science > Machine Learning arXiv:2603.24695 (cs) [Submitted on 25 Mar 2026] Title:Amplified Patch-Level Differential Privacy for Free via Random Cropping Authors:Kaan Durmaz, Jan Schuchardt, Sebastian Schmidt, Stephan Günnemann View a PDF of the paper titled Amplified Patch-Level Differential Privacy for Free via Random Cropping, by Kaan Durmaz and 3 other authors View PDF HTML (experimental) Abstract:Random cropping is one of the most common data augmentation techniques in computer vision, yet the role of its inherent randomness in training differentially private machine learning models has thus far gone unexplored. We observe that when sensitive content in an image is spatially localized, such as a face or license plate, random cropping can probabilistically exclude that content from the model's input. This introduces a third source of stochasticity in differentially private training with stochastic gradient descent, in addition to gradient noise and minibatch sampling. This additional randomness amplifies differential privacy without requiring changes to model architecture or training procedure. We formalize this effect by introducing a patch-level neighboring relation for vision data and deriving tight privacy bounds for differentially private stochastic gradient descent (DP-SGD) when combined with random cropping. Our analysis quantifies the patch inclusion probability and shows how it composes with minibatch sampling to yield a lower effective sampling rate....