STRM Privacy Diagnostics for reporting privacy metrics on a dataset.
Project description
STRM Privacy Diagnostics
This package contains diagnostics for your data, by means of computing k-Anonymity, l-Diversity and t-Closeness.
You can compute the scores by passing your data and indicating which columns are quasi-identifiers and sensitive attributes.
A 'quasi identifier' is a data attribute on an individual that together with other attributes could identify them. E.g. your length probably doesn't discern you from a larger group of people, but the combination of your length, age and city of birth will if someone has some knowledge about you.
A 'sensitive attribute' is a sensitive data point, like a specific medical diagnosis or credit score.
Installation
Install the package via Pip:
pip install strmprivacy-diagnostics
Usage
Simply import the package and
- point it to your input data
- calculate the statistics by passing the quasi identifiers and sensitive attributes
- print a report by passing the quasi identifiers and sensitive attributes
from strmprivacy.diagnostics import PrivacyDiagnostics
# create an instance of the diagnostics class
d = PrivacyDiagnostics("/path/to/csv")
# calculate the statistics
d.calculate_stats(
qi=['qi1', 'qi2', ...], # names of quasi identifier columns,
sa=['sa1', 'sa2', ...], # names of sensitive attributes
)
# create report
d.create_report(
qi=['qi1', 'qi2', ...], # names of quasi identifier columns,
sa=['sa1', 'sa2', ...], # names of sensitive attributes
)
d.stats
>>> {'k': xxx, 'l': {'col1': xxx, ...}, 't': xxx}
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