pycanon package

Subpackages

• pycanon.anonymity package
  • Subpackages
    • pycanon.anonymity.utils package
      • Submodules
      • pycanon.anonymity.utils.aux_anonymity module
      • pycanon.anonymity.utils.aux_functions module
      • Module contents
  • Module contents
    • alpha_k_anonymity()
    • basic_beta_likeness()
    • delta_disclosure()
    • enhanced_beta_likeness()
    • entropy_l_diversity()
    • k_anonymity()
    • l_diversity()
    • recursive_c_l_diversity()
    • t_closeness()
• pycanon.report package
  • Submodules
  • pycanon.report.base module
    • get_report_values()
  • pycanon.report.json module
    • get_json_report()
  • pycanon.report.pdf module
  • pycanon.report.pdf_utility_report module
  • Module contents
    • get_json_report()
    • get_report_values()
    • print_report()
• pycanon.utility package
  • Module contents
    • average_ecsize()
    • classification_metric()
    • discernability_metric()
    • sizes_ec()
    • stats_quasi_ident()

Submodules

pycanon.cli module

Module providing command line tools for pyCANON.

pycanon.cli.alpha_k_anonymity(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)

Calculate (alpha,k)-anonymity.

pycanon.cli.basic_beta_likeness(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)

Calculate basic beta-likeness.

pycanon.cli.delta_disclosure(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)

Calculate delta-disclosure.

pycanon.cli.enhanced_beta_likeness(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)

Calculate enhanced beta-likeness.

pycanon.cli.entropy_l_diversity(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)

Calculate entropy l-diversity.

pycanon.cli.k_anonymity(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>)

Calculate k-anonymity.

pycanon.cli.l_diversity(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)

Calculate l-diversity.

pycanon.cli.main(version: ~typing.Optional[bool] = <typer.models.OptionInfo object>)

Check the level of anonymity of a dataset.

pycanon.cli.recursive_c_l_diversity(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)

Calculate recursive (c,l)-diversity.

pycanon.cli.report(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)

Generate a complete privacy report.

pycanon.cli.t_closeness(filename: ~pathlib.Path = <typer.models.ArgumentInfo object>, qi: ~typing.List[str] = <typer.models.OptionInfo object>, sa: ~typing.List[str] = <typer.models.OptionInfo object>, gen: bool = <typer.models.OptionInfo object>)

Calculate t-closeness.

pycanon.cli.version_callback(version: bool)

Return version info.

Module contents

pyCANON is a library to check the values of the most common data privacy models.