Differentially Private Database Release via Kernel Mean Embeddings
Authors: Matej Balog, Ilya Tolstikhin, Bernhard Schölkopf
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | as well as basic empirical illustrations of their performance on synthetic datasets |
| Researcher Affiliation | Academia | 1MPI-IS, T ubingen, Germany 2University of Cambridge, UK. |
| Pseudocode | Yes | Algorithm 1 Differentially private database release via a synthetic data subspace of the RKHS |
| Open Source Code | Yes | Code: https://github.com/matejbalog/RKHS-private-database/ |
| Open Datasets | No | The paper uses synthetic datasets generated from a mixture of two Gaussians for its empirical illustrations, but does not provide concrete access information (link, DOI, formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper uses synthetic data for its experiments but does not provide specific dataset split information (percentages, counts, or detailed methodology) for training, validation, or testing its own models. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | Appendix B (Experimental Details) specifies the generation of synthetic datasets, the use of a Gaussian kernel with bandwidth σ = 1, the number of private data points N = 100,000, and for Algorithm 2, the number of random features J = 2000. |