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Anonymization Tool. The ARX graphical anonymization tool is a platform-independent Java/SWT application. We provide self-contained binary installers, as well as executable jar files. Installer. The installers were built with BitRocks InstallBuilder. Windows 64-Bit MacOS 64-Bit Linux/GTK 64-Bit. Example Project. This project file can be used to ...
- Overview
ARX Anonymization Tool. The graphical frontend of ARX...
- Publications
Design and Evaluation of a Data Anonymization Pipeline to...
- Contributors
All statistics start from 2013-05-10, which means that the...
- Exploration
Apart from some shortcuts to menu entries, ARX's application...
- Dependencies
Risk analyses and risk-based anonymization. Newton-Raphson:...
- Changelog
Tests: Add test cases for anonymization with data subsets;...
- Privacy Models
Supported privacy models. Three types of privacy threats are...
- Configuration
Performing the anonymization. ARX provides a dedicated...
- Overview
It provides fast identification and anonymization modules for private entities in text and images such as credit card numbers, names, locations, social security numbers, bitcoin wallets, US phone numbers, financial data and more.
ARX is a comprehensive open source software for anonymizing sensitive personal data. It supports a wide variety of (1) privacy and risk models, (2) methods for transforming data and (3) methods for analyzing the usefulness of output data.
It provides fast identification and anonymization modules for private entities in text such as credit card numbers, names, locations, social security numbers, bitcoin wallets, US phone numbers, financial data and more.
ARX is a comprehensive open source data anonymization tool aiming to provide scalability and usability. It supports various anonymization techniques, methods for analyzing data quality and re-identification risks and it supports well-known privacy models, such as k-anonymity, l-diversity, t-closeness and differential privacy. - arx-deidentifier/arx
During the anonymization process, ARX characterizes a solution space of potential transformations of the input dataset. For each solution candidate it is determined whether risk thresholds are met and data quality is quantified according to the given model.
23 maj 2022 · Find out the best data anonymization tools and techniques to hide and redact sensitive information from documents. BOOK A FREE DEMO.