Differentially Private Time Series Wasserstein Generative Adversarial Network for Private and Utilizable Synthetic Time Series Data Generation

The humongous volumes of data utilized to train the machine learning models are vulnerable to leakage by model inversion attacks and membership inference attacks. These days, massive amounts of research are being conducted to leverage differential privacy to safeguard the privacy of users. Tabular data generation from differentially private generative adversarial networks is still an untapped area. This work suggests a framework to enhance privacy protection in generating synthetic data by utilizing Wasserstein distance. The developed architecture generated synthetic data that replicated the time series relations of real-world data without compromising identifiable features of members of the input data. Results obtained from the architecture were compared with two other current GAN frameworks, DP-WGAN, and Time GAN. The privacy vs. utility tradeoff was found to be improved in the case of the architecture under discussion, as can be seen from the RMSE scores and Overall Quality Report.