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The OpenDP Library
The OpenDP Library is an open-source, modular collection of statistical algorithms for differential privacy. It is the core library of the OpenDP Project, a community effort to build trustworthy tools for analysing sensitive data. Differential privacy protects individuals by adding carefully calibrated random noise to results, so that no single person's data can be identified. The library lets developers build applications that analyse sensitive data while providing a formal, measurable privacy guarantee. It supports several different mathematical models of privacy, so users can choose the one that best fits their needs.
The library's design is based on a published programming framework for expressing privacy-aware computations. Users build an analysis from small components. Some components transform data, for example by clipping or aggregating values. Others add noise to release a private result. The library tracks how each step affects the privacy guarantee, so the overall privacy cost of a complete analysis can be calculated and controlled. This modular approach makes it easier to build new analyses and to check that they are correct.
OpenDP is implemented in Rust for speed and safety, with bindings that allow it to be used from Python and R. It is part of the OpenDP Commons, whose governance commits to having at least two active maintainers, reviewing all changes and conducting annual independent health checks. The library is still under development, and the developers advise evaluating its documented limitations before using it in privacy-critical applications. It is released under the MIT licence.
Auto-discovered on 2026-08-05 by OECD Catalogue Automation
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