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privgem

privgem: Privacy-Preserving Generative Models

https://github.com/kasra-hosseini/privgem

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
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    DOI references
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    Academic publication links
    Links to: zenodo.org
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    Scientific vocabulary similarity
    Low similarity (9.9%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

privgem: Privacy-Preserving Generative Models

Basic Info
  • Host: GitHub
  • Owner: kasra-hosseini
  • License: mit
  • Language: Python
  • Default Branch: develop
  • Homepage:
  • Size: 397 KB
Statistics
  • Stars: 3
  • Watchers: 2
  • Forks: 1
  • Open Issues: 6
  • Releases: 2
Created over 5 years ago · Last pushed over 4 years ago
Metadata Files
Readme License Citation

README.md


privgem

Privacy-Preserving Generative Models

Binder Continuous integration badge DOI

Credits

privgem uses codes from other libraries as listed below. ⚠️ Please read the list carefully and cite the original codes/papers as well.

| Method | Original version | Notes | |-------------|-----------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------| | PATE-CTGAN | smartnoise-sdk | tabular_patectgan of privgem is based on smartnoise-sdk with some minor changes (e.g., data preproc., logging, plotting and etc). | | DP-CTGAN | smartnoise-sdk | tabular_dpctgan of privgem is based on smartnoise-sdk with some minor changes (e.g., data preproc., logging, plotting and etc). | | Private-PGM | private-data-generation | tabular_ppgm of privgem is based on private-data-generation with some minor changes. | | CTGAN | sdv-dev | currently, privgem uses a forked version of ctgan, link. |

Installation

We strongly recommend installation via Anaconda:

bash conda create -n privgem_py38 python=3.8

  • Activate the environment:

bash conda activate privgem_py38

  • Clone privgem source code:

bash git clone https://github.com/kasra-hosseini/privgem.git

  • Install privgem dependencies:

bash pip install -r requirements.txt

  • Finally, install privgem library:

cd /path/to/privgem pip install -v -e .

Alternatively:

cd /path/to/privgem python setup.py install

  • To allow the newly created privgem_py38 environment to show up in Jupyter Notebook:

bash python -m ipykernel install --user --name privgem_py38 --display-name "Python (privgem_py38)"

Owner

  • Name: Kasra Hosseini
  • Login: kasra-hosseini
  • Kind: user
  • Location: London, UK

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use privgem, please cite it as below."
authors:
- family-names: "Hosseini"
  given-names: "Kasra"
  orcid: "https://orcid.org/0000-0003-4396-6019"
title: "privgem"
version: 0.1.1
doi: 10.5281/zenodo.5903696
date-released: 2022-01-25
url: "https://github.com/kasra-hosseini/privgem"

GitHub Events

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Last Year

Issues and Pull Requests

Last synced: over 1 year ago

All Time
  • Total issues: 11
  • Total pull requests: 5
  • Average time to close issues: about 1 month
  • Average time to close pull requests: 1 day
  • Total issue authors: 1
  • Total pull request authors: 2
  • Average comments per issue: 0.09
  • Average comments per pull request: 0.0
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • kasra-hosseini (11)
Pull Request Authors
  • kasra-hosseini (4)
  • OscartGiles (1)
Top Labels
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Dependencies

requirements.txt pypi
  • PuLP <=2.5
  • dython <0.7
  • jupyter-client >=6.1.5
  • jupyter-core >=4.6.3
  • jupyterlab <3.2
  • matplotlib <3.5
  • networkx <2.7
  • opacus >=0.9.0,<1
  • sdv <0.13
  • shap <0.40
  • torch >=1.6.0,<1.9
setup.py pypi
  • PuLP <=2.5
  • ctgan *
  • dython <0.7
  • jupyter-client >=6.1.5
  • jupyter-core >=4.6.3
  • jupyterlab <3.2
  • matplotlib <3.5
  • networkx <2.7
  • opacus >=0.9.0,<1
  • sdv <0.13
  • shap <0.40
  • torch >=1.6.0,<1.9