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:
pip install ppdp-anonops
Usage
Look at the following directory in the Github project to see the samples of usage: "ppdp-anonops/tests"
Metadata
Release files for ppdp-anonops 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| ppdp_anonops-0.1.1.tar.gz | 15.2 kB | Without provenance | Details for ppdp_anonops-0.1.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| ppdp_anonops-0.1.1-py3-none-any.whl | Python 3 | none | any | Details for ppdp_anonops-0.1.1-py3-none-any.whl |
Total release size: 51.3 kB
Release files / ppdp_anonops-0.1.1.tar.gz
| Download URL | ppdp_anonops-0.1.1.tar.gz |
|---|---|
| Size | 15.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / ppdp_anonops-0.1.1-py3-none-any.whl
| Download URL | ppdp_anonops-0.1.1-py3-none-any.whl |
|---|---|
| Size | 36.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.4.2 requests/2.25.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.52.0 CPython/3.6.5
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Security for ppdp-anonops 0.1.1
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