Anonymization methods for network security.

Anonymizing Library for Apache Spark

Repertoire sur l'anonymisation

pyCANON is a Python library and CLI to assess the values of the parameters associated with the most common privacy-preserving techniques.

Anonymization library for python. Protect the privacy of individuals.

ANJANA is a Python library for anonymizing sensitive data

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.

Very simple algorithm

In an age of widespread data collecting and sharing, the safeguarding of people’s sensitive information has become critical. Facial photos and tabular data frequently contain personal information that, if revealed, can lead to identity theft, discrimination, and other types of harm.

Comparison of the performance of machine learning models applied on anonymized data with different techniques

Prink (Privacy-Preserving Flink) is a data anonymization solution for Apache Flink, that provides k-anonymity and l-diversity for data streams.

DB security Semester Homework - MSc Fall 2020

DataArmor is a cutting-edge tool focused on safeguarding privacy in today's data-driven world using K-anonymity L-diversity and t-closeness privacy model. As the sharing of personal and microdata grows, ensuring the protection of individual identities during data publication and analysis becomes essential.

Implementation Codes/Functions used in the Data-centric L-diversity model (Synthetic data-aided anonymization model).

A k-anonymity and l-diversity problem with the Adult data

Application of K-Anonymity, L-Diversity, T-Closeness on numerical or categorial Data.

This repository contains Python scripts to identify attributes in a dataset and subsequently determine the best QID dimension based on privacy gain and non-uniform entropy.

Study of an article in the context of the course "Data anonymization and privacy". About t-closeness, l-diversity and k-anonymity. Implementation of the associated code.