Synthesizing Differentially Private Datasets using Random Mixing

The goal of differentially private data publishing is to release a modified dataset so that its privacy can be ensured while allowing for efficient learning. We propose a new data publishing algorithm in which a released dataset is formed by mixing ℓ randomly chosen data points and then perturbing them with an additive noise. Our privacy analysis shows that as ℓ increases, noise with smaller variance is sufficient to achieve a target privacy level. In order to quantify the usefulness of our algorithm, we adopt the accuracy of a predictive model trained with our synthetic dataset, which we call the utility of the dataset. By characterizing the utility of our dataset as a function of ℓ, we show that one can learn both linear and nonlinear predictive models so that they yield reasonably good prediction accuracies. Particularly, we show that there exists a sweet spot on ℓ that maximizes the prediction accuracy given a required privacy level, or vice versa. We also demonstrate that given a target privacy level, our datasets can achieve higher utility than other datasets generated with the existing data publishing algorithms.