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Exact and Efficient Bayesian Inference for Privacy Risk Quantification (Accompanying Artifact)

Dataset:
Dataset Types
Software

Dataset Information

Date made available

2023-07-22

Publisher

ZENODO

Description

The artifact consists of a virtual machine with all necessary software to execute the code accompanying in the paper's GitHub repository: https://github.com/itu-square/gauss-privug. The repository contains a proof-of-concept implementation of our inference engine. All the experiments in the paper are included here. For convenience, they are presented in a Jupyter notebook with further comments. The experiments generate all the evaluation plots in the paper.

Cite this dataset

Brief description
Rasmus C. Rønneberg, Raúl Pardo, Andrzej Wąsowski. (2023). Exact and Efficient Bayesian Inference for Privacy Risk Quantification (Accompanying Artifact). 10.5281/zenodo.8173905