Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
The Bernstein Mechanism: Function Release under Differential Privacy
Authors: Francesco Ald, Benjamin Rubinstein
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Competitive rates are demonstrated for kernel density estimation; and ε-differential privacy is achieved for a broader class of support vector machines than known previously. ... In Figure 1 we display the utility (averaged over 1000 repeats) of the Bernstein mechanism (k = 20) on 5000 points drawn from a mixture of two normal distributions N(0.5, 0.02) and N(0.75, 0.005) with weights 0.4, 0.6, respectively. ... Figure 2 depicts SVM learning with RBF kernel (C = σ = 1) on 1500 each of positive (negative) Gaussian data with mean [0.3, 0.5] ([0.6, 0.4]) and covariance [0.01, 0; 0, 0.01] (0.01 [1, 0.8; 0.8, 1.5]) and demonstrates the mechanism’s uniform approximation of predictions, best seen geometrically with the classifier’s decision boundary. |
| Researcher Affiliation | Academia | Francesco Alda Horst Gortz Institute for IT Security and Faculty of Mathematics Ruhr-Universitat Bochum, Germany EMAIL Benjamin I.P. Rubinstein Dept. Computing and Information Systems The University of Melbourne, Australia EMAIL |
| Pseudocode | Yes | Algorithm 1 The Bernstein mechanism |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | No | The paper mentions generating synthetic data for examples (e.g., "5000 points drawn from a mixture of two normal distributions" for KDE, and "1500 each of positive (negative) Gaussian data" for SVM), but it does not provide access information (link, citation, repository) for these or any other public datasets. |
| Dataset Splits | No | The paper mentions using training data but does not specify any training/validation/test splits, percentages, or methodology for data partitioning for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used. |
| Experiment Setup | Yes | Figure 2 depicts SVM learning with RBF kernel (C = σ = 1) |