PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage
This addresses privacy risks for data owners in distributed machine learning, offering a defense against gradient inversion attacks without the typical trade-offs of perturbation methods.
The paper tackles the problem of data reconstruction from gradients in collaborative neural network training by introducing PRECODE, a generic privacy-enhancing module that uses variational modeling to prevent gradient leakage. The result is a reduction of attack success rate to 0% with minimal impact on model performance.
Collaborative training of neural networks leverages distributed data by exchanging gradient information between different clients. Although training data entirely resides with the clients, recent work shows that training data can be reconstructed from such exchanged gradient information. To enhance privacy, gradient perturbation techniques have been proposed. However, they come at the cost of reduced model performance, increased convergence time, or increased data demand. In this paper, we introduce PRECODE, a PRivacy EnhanCing mODulE that can be used as generic extension for arbitrary model architectures. We propose a simple yet effective realization of PRECODE using variational modeling. The stochastic sampling induced by variational modeling effectively prevents privacy leakage from gradients and in turn preserves privacy of data owners. We evaluate PRECODE using state of the art gradient inversion attacks on two different model architectures trained on three datasets. In contrast to commonly used defense mechanisms, we find that our proposed modification consistently reduces the attack success rate to 0% while having almost no negative impact on model training and final performance. As a result, PRECODE reveals a promising path towards privacy enhancing model extensions.