# Private Set Generation with Discriminative Information Source: [Private Set Generation with Discriminative Information]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:44:11.594Z Authors: Dingfan Chen, Raouf Kerkouche, Mario Fritz Published: 2022-11-07T10:02:55.000Z ## Abstract Differentially private data generation techniques have become a promising solution to the data privacy challenge -- it enables sharing of data while complying with rigorous privacy guarantees, which is essential for scientific progress in sensitive domains. Unfortunately, restricted by the inherent complexity of modeling high-dimensional distributions, existing private generative models are struggling with the utility of synthetic samples. In contrast to existing works that aim at fitting the complete data distribution, we directly optimize for a small set of samples that are representative of the distribution under the supervision of discriminative information from downstream tasks, which is generally an easier task and more suitable for private training. Our work provides an alternative view for differentially private generation of high-dimensional data and introduces a simple yet effective method that greatly improves the sample utility of state-of-the-art approaches. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on MNIST (test) (Accuracy with Epsilon Constraints)](): Image Classification; MNIST (test); Accuracy (ε=10): 95.6. ## Original sources and implementation [Original paper]() [PDF]()