Differentially Private Synthetic Data Generation: A Review of Recent Approaches

Generative models are a hot topic in machine learning, allowing us to create synthetic data that mimics real-world situations. However, these models need a lot of training data, which can be challenging when dealing with sensitive information like medical records or financial data. Differential privacy is a way to protect people’s personal information while still using this data for machine learning. In this article, we evaluate the effectiveness of generative models in creating synthetic data for the Adult dataset, which contains information about people’s age, occupation, and more. We compare different models and show that one called DP-SACTGAN performs the best in terms of generating useful data while also protecting privacy.
To understand how these models work, let’s think of them like cooks in a kitchen. Just as chefs use various ingredients to create delicious meals, generative models take different bits of information and combine them to make synthetic data that resembles the real thing. However, just as we wouldn’t want our personal information shared with others without permission, these models need to be careful not to reveal too much about any individual in the training data. Differential privacy helps ensure this by adding noise to the data so that no single person can be identified.
We test these models using a measure called the Wasserstein Distance (WD) and Jensen-Shannon Divergence (JSD), which help us evaluate how well the generated data resembles the original Adult dataset. We also look at how well the models perform under attack, as an adversary might try to manipulate the synthetic data to their advantage.
Our results show that DP-SACTGAN outperforms other models in terms of generating useful data while maintaining privacy. This is because it includes an auxiliary classifier module that helps the model learn from small amounts of training data. Additionally, we find that DP-SACTGAN has better performance under attack than other models, which suggests that it’s more robust and can handle unexpected situations better.
In summary, generative models have the potential to revolutionize how we work with sensitive information like medical records or financial data. By using differential privacy techniques like those in DP-SACTGAN, we can create synthetic data that’s both useful for machine learning and protects people’s personal information.