SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing
This work addresses the accuracy gap between private and non-private image classification, which is an incremental improvement for privacy-preserving machine learning.
The paper tackles the performance degradation in differentially private stochastic gradient descent (DPSGD) for image recognition by proposing SA-DPSGD, a simulated annealing-based method that improves test accuracies, achieving 98.35% vs. 98.12% on MNIST and 60.92% vs. 59.34% on CIFAR10 under the same hyperparameters.
Differential privacy (DP) provides a formal privacy guarantee that prevents adversaries with access to machine learning models from extracting information about individual training points. Differentially private stochastic gradient descent (DPSGD) is the most popular training method with differential privacy in image recognition. However, existing DPSGD schemes lead to significant performance degradation, which prevents the application of differential privacy. In this paper, we propose a simulated annealing-based differentially private stochastic gradient descent scheme (SA-DPSGD) which accepts a candidate update with a probability that depends both on the update quality and on the number of iterations. Through this random update screening, we make the differentially private gradient descent proceed in the right direction in each iteration, and result in a more accurate model finally. In our experiments, under the same hyperparameters, our scheme achieves test accuracies 98.35%, 87.41% and 60.92% on datasets MNIST, FashionMNIST and CIFAR10, respectively, compared to the state-of-the-art result of 98.12%, 86.33% and 59.34%. Under the freely adjusted hyperparameters, our scheme achieves even higher accuracies, 98.89%, 88.50% and 64.17%. We believe that our method has a great contribution for closing the accuracy gap between private and non-private image classification.