Amplified Patch-Level Differential Privacy for Free via Random Cropping

TL;DR

This paper reveals that random cropping in computer vision acts as an additional source of stochasticity in differentially private training, which amplifies privacy guarantees without altering the training process.

Contribution

The authors formalize the privacy amplification effect of random cropping at the patch level and derive tight bounds for DP-SGD, demonstrating improved privacy-utility trade-offs empirically.

Findings

Random cropping probabilistically excludes sensitive content, enhancing privacy.

Patch-level analysis quantifies the amplification effect.

Empirical results show improved privacy-utility trade-offs across datasets.

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. Empirically, we validate that patch-level amplification improves the privacy-utility trade-off across multiple segmentation architectures and datasets. Our results demonstrate that aligning privacy accounting with domain structure and additional existing sources of randomness can yield stronger guarantees at no additional cost.