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.
-
Updated
Jun 2, 2024 - Python
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.
Go wrapper service for the STAR randomness server.
ANJANA is a Python library for anonymizing sensitive data
Caterpillar Proxy - The simple and parasitic web proxy with SPAM filter (formerly, php-httpproxy)
Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)
A repo that takes you through some principles about data privacy based on the Kenya Data Protection Act and General Data Protection Regulation. Useful for a data person.
(ε, k)-Randomized Anonymization: ε-Differentially Private Data Sharing with k-Anonymity
Exploring US Census microdata, tackling privacy issues, and anonymization. Exercise A delves into quasi-identifiers, anonymization methods, identification risks, and differential privacy. Exercise B involves data loading, k-anonymity, histograms, adding noise for privacy, computing private averages, and analyzing privacy parameter impacts.
Scalable distributed data anonymization for large datasets
An application of the "Mondrian Multidimensional K-Anonymity" article in Python
pyCANON is a Python library and CLI to assess the values of the parameters associated with the most common privacy-preserving techniques.
The command line tool 'pwnedk' checks whether a particular password is leaked applying k-anonymity via the API "Searching by range" of HIBP.
Data privacy
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.
Anonymizing Library for Apache Spark
Evaluating variety of k-Anonymity techniques.
Prink (Privacy-Preserving Flink) is a data anonymization solution for Apache Flink, that provides k-anonymity and l-diversity for data streams.
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.
Capstone Project at HCMUT of System for Preserving Privacy in Data Sharing
Transparently check if a password has been dumped in a breach
Add a description, image, and links to the k-anonymity topic page so that developers can more easily learn about it.
To associate your repository with the k-anonymity topic, visit your repo's landing page and select "manage topics."