RRN: A differential private approach to preserve privacy in image classification

Abstract

AbstractThe wide application of image classification has given rise to many intelligent systems, such as face recognition systems, which makes our life more convenient. However, the ensuing privacy leakage problem has become increasingly serious. The training of a deep neural network requires lots of data, which may contain sensitive information of users and may be exploited by data collectors. A perturbation algorithm named RRN is proposed for image data based on local differential privacy, which provides a rigorous privacy guarantee. Existing solutions have low accuracy due to the high sensitivity of an image; the authors' method combines the Randomized Response mechanism with the Laplace mechanism to solve this problem. Experiments were conducted on the MNIST and CIFAR‐10 datasets to show the effectiveness of the algorithm. Experimental results shows that the model is better than baseline models. The algorithm was also implemented on the commonly used model in deep learning, the VGG model, which can achieve 96.4% accuracy in the non‐private version on the CIFAR‐10 dataset. The accuracy of the differential private VGG model based on the RRN algorithm is 83% when , which is still excellent. The experimental results show that the RRN algorithm can both preserve privacy and data utility.