Use Our Tools

OpenDP Library

The OpenDP Library is a modular collection of statistical algorithms that adhere to the definition of differential privacy. It can be used to build applications of privacy-preserving computations, using a number of different models of privacy. OpenDP is implemented in Rust, with bindings for easy use from Python.

The architecture of the OpenDP Library is based on a conceptual framework for expressing privacy-aware computations. This framework is described in the paper A Programming Framework for OpenDP.

The OpenDP Library is part of the larger OpenDP Project, a community effort to build trustworthy, open source software tools for analysis of private data. (For simplicity in these docs, when we refer to “OpenDP,” we mean just the library, not the entire project.)

The full documentation for OpenDP is located at Here are some helpful entry points:


OpenDP Commons

The OpenDP Commons is a community-driven layer of OpenDP based on a common differential privacy library. It consists of tools and packages for building end-to-end differentially private systems. The governance for this layer facilitates contributions and vetting by the community, as well as reviews, guidance, and guarantees for using the library and tools.


Please contact us if you are looking into building tools with OpenDP.


The diagram below illustrates how the OpenDP library is the foundation of the OpenDP Commons and how various tools are built on top.





DP Creator

DP Creator is an open-source web-based application that demonstrates how the OpenDP library can be seamlessly integrated with widely-implemented large-scale research data repositories, like Dataverse, via a user-friendly interface that allows data owners and analysts to produce DP statistics without writing any code. The application incorporates step-by-step instructions to guide new users in learning the basics of tuning parameters to generate useful DP statistics.


The source code of DP Creator can be found at and you can try a live demo version here:   You can also watch the video of an overview on DP Creator at our last community meeting. Please share your feedback with us at

DP Creator - Create Statistic

The SmartNoise System

SmartNoise is jointly developed by Microsoft and Harvard's Institute for Quantitative Social Science (IQSS) and the School of Engineering and Applied Sciences (SEAS) as part of the Open Differential Privacy (OpenDP) initiative. The project aims to connect solutions from the research community with the lessons learned from real-world deployments to make Differential Privacy broadly accessible. 

Building upon the foundation of the OpenDP library, the SmartNoise SDK includes two Python packages: 

  • smartnoise-sql - Allows data owners to run differentially private SQL queries. For use, when generating reports or data cubes over tabular data stored in SQL databases or Spark, or when the data are very large.

  • smartnoise-synth - Provides utilities for generating differentially private synthetic datasets. Useful  when you can’t predict the workload in advance, and want to be able to share “looks like” data with collaborators.

Both of these packages focus on the "global model" of Differential Privacy, as opposed to the "local model." In the global Differential Privacy model, a trusted data collector is presumed to have access to unprotected data and wishes to protect public releases of information.  

To Learn More About SmartNoise, Please Visit:

SmartNoise SDK GitHub repository with instructions and examples of installation and usage

Microsoft SmartNoise Differential Privacy Machine Learning Case Studies