The Data Anonymization Library is a lightweight source library designed for developers and researchers to integrate fundamental data anonymization techniques into their projects. Built on foundational research, this library offers essential tools for data anonymization, synthetic data generation, and secure multi-party computation in a flexible and accessible format.

Developed in C and Python, the library is well-suited for users who are exploring anonymization solutions or developing early-stage applications. It provides a modular design that allows for straightforward integration into different environments, from small standalone projects to larger privacy-conscious systems.

• Core Anonymization Tools

The library includes key anonymization methods such as k-anonymity and basic synthetic data generation. These tools enable developers to implement privacy measures while maintaining some level of data utility for testing and research.

• Basic Risk Assessment

A preliminary risk assessment feature allows users to input dataset details like type, size, and structure to receive an overview of potential de-anonymization risks. While simple, it provides a starting point for understanding privacy vulnerabilities.

• Bias Mitigation and Secure Computation

Basic functionality for secure multi-party computation and bias mitigation is available, helping developers test secure collaboration and fairness in datasets with minimal setup.

• Modular and Customizable

The library is designed to be lightweight and modular, allowing for easy customization and expansion of the core features to suit specific project needs or test new techniques as they evolve.