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This article discusses the use of differential privacy in image recognition and how the existing methods for differentially private deep learning need to be improved. To address this problem, a research team from Shanghai University in China suggests a simulated annealing-based differentially private stochastic gradient descent (SA-DPSGD) approach that accepts a candidate update with a probability that depends on the quality of the update and the number of iterations.
