A Deep Learning-based Data Usability Enhancement Scheme for Differential Privacy
Haonan Yan, Xiaoguang Li, Gewei Zheng, Hui Li, Fenghua Li, Xiaodong Sheldon Lin · 2023
While differential privacy (DP) is widely used to ensure privacy, it can also significantly reduce data accuracy. Current research attempts to improve accuracy by leveraging post-processing techniques, but these methods are sub-optimal and only applicable to specific data types. To address the issue, in this work, we propose a novel deep learning-based data usability enhancement method for differential privacy that is data-type independent. By using image denoising technology, the proposed scheme reduces the mean square error (MSE) of DP-perturbed data and theoretically justifies the relationship between data noise reduction and image noise reduction. The effectiveness of the proposed scheme is demonstrated through a comprehensive evaluation.