semvec/pypi-packages huggingface dataset:
ppdp-anonops 0.1.1
This project implemets basic anonymization operations for event data which are used by process mining techniques.

## 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"
p-tlkc-privacy 0.0.14
TLKC-privacy model for process mining

## Introduction
This project implements the TLKC-privacy model proposed in the paper [TLKC-Privacy Model for Process Mining](https://www.researchgate.net/publication/340261780_TLKC-Privacy_Model_for_Process_Mining).
## 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 p-tlkc-privacy
```

## Usage

```python
from p_tlkc_privacy.privacyPreserving import privacyPreserving

event_log = "running_example.xes"

L = [2]
C = [1]
K = [1]
K2 = [0.1]
# sensitive = ['creator']
sensitive = []
T = ["minutes"]
cont = []
bk_type = "sequence" #set, multiset, sequence, relative

privacy_aware_log_dir = "xes_results"
privacy_aware_log_path = "test.xes"

pp = privacyPreserving(event_log, "example")
result = pp.apply(T, L, K, C, K2, sensitive, cont, bk_type, privacy_aware_log_dir, privacy_aware_log_path)

print(result)
```