DPAdaMod_AGC: Adaptive Gradient Clipping-Based Differential Privacy

Differential privacy is a promising framework for computing on sensitive data while preserving privacy. However, balancing privacy and accuracy remains a significant challenge. In this paper, the DPAdaMod_AGC algorithm is proposed to address the above challenge, which uses adaptive gradient clipping to improve the accuracy of deep learning models without compromising privacy. By reducing the inclusion of nonessential noise during training, the proposed algorithm combines stochastic gradient descent with gradient clipping striking an effective trade-off between privacy and accuracy. Simulation analysis shows that compared with the DPAdaMod algorithm, the algorithm proposed in this paper not only improves the classification accuracy by 2.3%, but also consumes less privacy budget while protecting privacy. The results demonstrate the superiority of the DPAdaMod_AGC algorithm in achieving both privacy and accuracy goals.