anonypyx 0.2.1

Anonymization library for python, fork of anonypy

AnonyPyx

This is a fork of the python library AnonyPy providing data anonymization techniques. AnonyPyx adds further algorithms (see below) and introduces a declarative interface. If you consider migrating from AnonyPy, keep in mind that AnonyPyx is not compatible with its original API.

Features

• partion-based anonymization algorithm Mondrian [1] supporting
  • k-anonymity
  • l-diversity
  • t-closeness
• microclustering based anonymization algorithm MDAV-Generic [2] supporting
  • k-anonymity
• interoperability with pandas data frames
• supports both continuous and categorical attributes

Install

pip install anonypyx

Usage

Disclaimer: AnonyPyX does not shuffle the input data currently. In some applications, records can be re-identified based on the order in which they appear in the anonymized data set when shuffling is not used.

Mondrian:

import anonypyx
import pandas as pd

# Step 1: Prepare data as pandas data frame:

columns = ["age", "sex", "zip code", "diagnosis"]
data = [
    [50, "male", "02139", "stroke"],
    [33, "female", "10023", "flu"],
    [66, "intersex", "20001", "flu"],
    [28, "female", "33139", "diarrhea"],
    [92, "male", "94130", "cancer"],
    [19, "female", "96850", "diabetes"],
]

df = pd.DataFrame(data=data, columns=columns)

for column in ("sex", "zip code", "diagnosis"):
    df[column] = df[column].astype("category")

# Step 2: Prepare anonymizer

anonymizer = anonypyx.Anonymizer(df, k=3, l=2, algorithm="Mondrian", feature_columns=["age", "sex", "zip code"], sensitive_column="diagnosis")

# Step 3: Anonymize data (this might take a while for large data sets)

anonymized_records = anonymizer.anonymize()

# Print results:

anonymized_df = pd.DataFrame(anonymized_records)
print(anonymized_df)

Output:

     age            sex           zip code diagnosis  count
0  19-33         female  10023,33139,96850  diabetes      1
1  19-33         female  10023,33139,96850  diarrhea      1
2  19-33         female  10023,33139,96850       flu      1
3  50-92  male,intersex  02139,20001,94130    cancer      1
4  50-92  male,intersex  02139,20001,94130       flu      1
5  50-92  male,intersex  02139,20001,94130    stroke      1

MDAV-generic

# Step 2: Prepare anonymizer
anonymizer = anonypyx.Anonymizer(df, k=3, algorithm="MDAV-generic", feature_columns=["age", "sex", "zip code"], sensitive_column="diagnosis")

Contributing

Clone the repository:

git clone https://github.com/questforwisdom/anonypyx.git

Set a virtual python environment up and install dependencies:

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Run tests:

pytest

Changelog

0.2.0

• added the microaggregation algorithm MDAV-generic [2]
• added the Anonymizer class as the new API
• removed Preserver class which was superseded by Anonymizer

0.2.1

• minor bugfixes

References

• [1]: LeFevre, K., DeWitt, D. J., & Ramakrishnan, R. (2006). Mondrian multidimensional K-anonymity. 22nd International Conference on Data Engineering (ICDE’06), 25–25. https://doi.org/10.1109/ICDE.2006.101
• [2]: Domingo-Ferrer, J., & Torra, V. (2005). Ordinal, continuous and heterogeneous k-anonymity through microaggregation. Data Mining and Knowledge Discovery, 11, 195–212.