https://github.com/strmprivacy/strm-privacy-diagnostics

A simple Python package to quickly run privacy metrics for your data. Obtain the K-anonimity, L-diversity and T-closeness to asses how anonymous your transformed data is, and how it's balanced with data usability.
https://github.com/strmprivacy/strm-privacy-diagnostics

k-anonymity l-diversity nist privacy-enhancing-technologies privacy-tools t-closeness

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A simple Python package to quickly run privacy metrics for your data. Obtain the K-anonimity, L-diversity and T-closeness to asses how anonymous your transformed data is, and how it's balanced with data usability.

• Host: GitHub
• URL: https://github.com/strmprivacy/strm-privacy-diagnostics
• Owner: strmprivacy
• Created: 2022-08-29T15:50:31.000Z (about 2 years ago)
• Default Branch: master
• Last Pushed: 2023-07-11T08:54:38.000Z (over 1 year ago)
• Last Synced: 2023-07-11T09:35:21.262Z (over 1 year ago)
• Topics: k-anonymity, l-diversity, nist, privacy-enhancing-technologies, privacy-tools, t-closeness
• Language: Python
• Homepage: https://getstrm.com
• Size: 553 KB
• Stars: 7
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md

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README

        
 # 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.  

## Framework

You can use this package in the context of data privacy and -security frameworks, such as NIST, SOC2 or ISO 27001/27701. 

### NIST Privacy Framework

Leverage this package in the NIST Privacy Framework for the following sub-categories:

- CT.DP-P1: Data are processed in an unobservable or unlinkable manner (e.g., data actions take place on local devices, privacy-preserving cryptography).

- CT.DP-P2: Data are processed to limit the identification of individuals (e.g., de-identification privacy techniques, tokenization).

### ISO 27001 / 27701

We're doing an inventory of the sections in ISO 27001 / 27701 for which Privacy Diagnostics can be helpful - stay tuned!

### SOC2 type I/II

We're exploring the relevant categories in SOC2 type I/II for which you can leverage this Privacy Diagnostics package - stay tuned!

## 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

```python

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}

```