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

Authors: Rønneberg, Rasmus C.; Pardo, Raúl; Wąsowski, Andrzej;

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

Abstract

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. The password of the Zip file is: sefm_conference_2023

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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