Differentially Private Synthetic Data Using KD-Trees. (arXiv:2306.13211v1 [cs.CR])

Creation of a synthetic dataset that faithfully represents the data
distribution and simultaneously preserves privacy is a major research
challenge. Many space partitioning based approaches have emerged in recent
years for answering statistical queries in a differentially private manner.
However, for synthetic data generation problem, recent research has been mainly
focused on deep generative models. In contrast, we exploit space partitioning
techniques together with noise perturbation and thus achieve intuitive and
transparent algorithms. We propose both data independent and data …

challenge data distribution major privacy private problem research space synthetic synthetic data trees