Metadata-Version: 2.1
Name: ppdp-anonops
Version: 0.0.4
Summary: This project implemets basic anonymization operations for event data which are used by process mining techniques.
Home-page: https://github.com/m4jidRafiei/PPDP-AnonOps
Author: Alexander 'DevSchnitzel' Schnitzler
Author-email: DevSchnitzel@outlook.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: ==3.6
Description-Content-Type: text/markdown
Requires-Dist: kmodes (==0.10.2)
Requires-Dist: pm4py (==1.2.10)
Requires-Dist: p-privacy-metadata (==0.0.4)
Requires-Dist: numpy (>=1.18.1)
Requires-Dist: matplotlib (==2.2.2)
Requires-Dist: pycryptodome (==3.9.9)
Requires-Dist: scikit-learn (>=0.23.2)

## Introduction
This project implemets basic anonymization operations for event data which are used by process mining techniques. The anonymization operations are formally explained in the following paper: https://www.researchgate.net/publication/342048551_Privacy-Preserving_Data_Publishing_in_Process_Mining

Ref: implemeted by "Alexander 'DevSchnitzel' Schnitzler" as part of his bachelor thesis at PADS group.
## Python Package
The implementation has been published as a standard Python package. Use the following command to install the corresponding Python package:

```shell
pip install ppdp-anonops
```

## Usage
Look at the following directory in the Github project to see the samples of usage: "ppdp-anonops/tests"

