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Anonymization library for python. Protect the privacy of individuals.

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glassonion1/anonypy

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Anonymization library for python.AnonyPy provides following privacy preserving techniques for the anonymization.

  • K Anonymity
  • L Diversity
  • T Closeness

The Anonymization method

  • Anonymization method aims at making the individual record be indistinguishable among a group record by using techniques of generalization and suppression.
  • Turning a dataset into a k-anonymous (and possibly l-diverse or t-close) dataset is a complex problem, and finding the optimal partition into k-anonymous groups is an NP-hard problem.
  • AnonyPy uses "Mondrian" algorithm to partition the original data into smaller and smaller groups
  • The algorithm assumes that we have converted all attributes into numerical or categorical values and that we are able to measure the “span” of a given attribute Xi.

Install

$ pip install anonypy

Usage

importanonypyimportpandasaspddata= [    [6,"1","test1","x",20],    [6,"1","test1","x",30],    [8,"2","test2","x",50],    [8,"2","test3","w",45],    [8,"1","test2","y",35],    [4,"2","test3","y",20],    [4,"1","test3","y",20],    [2,"1","test3","z",22],    [2,"2","test3","y",32],]columns= ["col1","col2","col3","col4","col5"]categorical=set(("col2","col3","col4"))df=pd.DataFrame(data=data,columns=columns)fornameincategorical:df[name]=df[name].astype("category")feature_columns= ["col1","col2","col3"]sensitive_column="col4"p=anonypy.Preserver(df,feature_columns,sensitive_column)rows=p.anonymize_k_anonymity(k=2)dfn=pd.DataFrame(rows)print(dfn)

Original data

   col1 col2   col3 col4  col50     6    1  test1    x    201     6    1  test1    x    302     8    2  test2    x    503     8    2  test3    w    454     8    1  test2    y    355     4    2  test3    y    206     4    1  test3    y    207     2    1  test3    z    228     2    2  test3    y    32

The created anonymized data is below(Guarantee 2-anonymity).

  col1 col2         col3 col4  count0  2-4    2        test3    y      21  2-4    1        test3    y      12  2-4    1        test3    z      13  6-8    1  test1,test2    x      24  6-8    1  test1,test2    y      15    8    2  test3,test2    w      16    8    2  test3,test2    x      1

Publish PyPI

$ python -m pip install hatchling wheel twine$ python -m build --wheel .$ python -m twine upload dist/*

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Anonymization library for python. Protect the privacy of individuals.

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