On the Differential Privacy of Bayesian Inference
Authors: Zuhe Zhang, Benjamin Rubinstein, Christos Dimitrakakis
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Worked examples and experiments with Bayesian naïve Bayes and Bayesian linear regression illustrate the application of our mechanisms. ... Experiments Having proposed a number of mechanisms for approximating exact Bayesian inference in the general framework of probabilistic graphical models, we now demonstrate our approaches on two simple, well-known PGMs: the (generative) naïve Bayes classifier, and (discriminative) linear regression. |
| Researcher Affiliation | Academia | School of Mathematics and Statistics, Department of Computing and Information Systems, The University of Melbourne, Australia zhang.zuhe@gmail.com, brubinstein@unimelb.edu.au Christos Dimitrakakis Univ-Lille-3, France Chalmers University of Technology, Sweden christos.dimitrakakis@gmail.com |
| Pseudocode | Yes | Algorithm 1 Laplace Mechanism on Posterior Updates ... Algorithm 2 Laplace Mechanism in the Fourier Domain ... Algorithm 3 Mechanism for MAP Point Estimates |
| Open Source Code | No | No explicit statement or link providing access to the paper's source code. |
| Open Datasets | Yes | U.S. census records dataset from the Integrated Public Use Microdata Series (Minnesota Population Center 2009) with 370k records and 14 demographic features. ... https: //international.ipums.org accessed 2015-08-30. |
| Dataset Splits | No | The paper describes train and test splits, but no explicit mention of a separate validation set. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) are provided for the experimental setup. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., libraries, frameworks, or solvers) are mentioned. |
| Experiment Setup | Yes | We trained our mechanisms on only 50 examples, with uniform Beta priors. ... t was set for the Fourier approach, so that stealth was achieved 90% of the time those times that contributed to the plot. ... varying prior precision b (inverse of covariance) and weights with bounded norm 10/ b |