Renyi Differential Privacy Mechanisms for Posterior Sampling
Authors: Joseph Geumlek, Shuang Song, Kamalika Chaudhuri
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present the experimental results for our proposed algorithms for both exponential family and GLMs. Our experimental design focuses on two goals first, analyzing the relationship between λ and ϵ in our privacy guarantees and second, exploring the privacy-utility trade-off of our proposed methods in relation to existing methods. |
| Researcher Affiliation | Academia | Joseph Geumlek University of California, San Diego jgeumlek@cs.ucsd.edu Shuang Song University of California, San Diego shs037@eng.ucsd.edu Kamalika Chaudhuri University of California, San Diego kamalika@cs.ucsd.edu |
| Pseudocode | Yes | Algorithm 1 Direct Posterior... Algorithm 2 Diffused Posterior... Algorithm 3 Concentrated Posterior... Algorithm 4 Concentrated Posterior... Algorithm 5 Diffuse Posterior |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the methodology or links to a code repository. |
| Open Datasets | Yes | We conduct Bayesian logistic regression on three real datasets: Abalone, Adult and MNIST. |
| Dataset Splits | No | The paper states '1/3 of the each dataset is used for testing, and the rest for training.' and provides specific training and test sample counts, but does not explicitly mention a separate validation split or cross-validation strategy. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | For all algorithms, we use an original Gaussian prior with β = 10-3. The posterior sampling is done using slice sampling with 1000 burn-in samples. ... 500 iterations of binary search were used to select r and m when needed. ... For privacy parameters, we set λ = 1, 10, 100 and ϵ {e-5, e-4, . . . , e3}. |