linkageattackit

Linkageattackit is a software toolkit designed to study and quantify linkage attacks on anonymized data. It provides a framework for simulating how external information can be combined with supposedly de-identified data to re-identify individuals, enabling researchers and data custodians to assess re-identification risk and validate privacy protections.

The project originated in privacy research communities as part of reproducible evaluation of anonymization techniques. It is maintained as an open-source project with a focus on transparent methodology and community contributions. The initiative aims to standardize how linkage risks are measured and compared across different data-release scenarios.

Key features include a data generator for creating synthetic or semi-synthetic datasets, an attacker simulator that models adversaries with varying background knowledge, an end-to-end attack pipeline, and an analytics module that computes privacy metrics such as re-identification rate, linkage confidence, and information gain. It supports common anonymization schemes (k-anonymity, l-diversity) and can benchmark against differential privacy baselines. The toolkit emphasizes reproducibility and comparability of experiments across studies.

The architecture is modular: core services expose a Python API and a command-line interface; components communicate through configuration files and defined interfaces; it supports plugin extensions and integration with Jupyter notebooks for interactive exploration. Documentation covers setup, experiment design, and result interpretation to aid researchers and practitioners.

Usage and impact: researchers use linkageattackit to stress-test data release methods, compare protection techniques, and reproduce experiments. Data stewards and policy makers may use it to understand residual risk in published datasets and to inform governance decisions.

Ethics and safety: the project emphasizes responsible use, with licenses and guidelines that discourage misuse. It provides documentation on ethical considerations, responsible disclosure, and conditions for use with real data. See also: linkage attack, data anonymization, privacy-preserving data publishing.