Differential Privacy is a collection of libraries for generating differentially private statistics over datasets.
The project addresses the challenge of computing aggregate statistics while mathematically guaranteeing that individual records cannot be reverse-engineered from results. It provides multiple tools suited to different use cases and programming languages. Privacy on Beam offers an end-to-end framework for Go built on Apache Beam, designed for accessibility to non-experts. PipelineDP4j provides equivalent functionality for JVM languages and supports both Apache Beam and Apache Spark. Underlying these high-level frameworks are three DP building block libraries in C++, Go, and Java that implement the core noise addition primitives and differentially private aggregations. The project also includes a stochastic tester for catching regressions, a privacy budget accounting library, a command-line interface for running differentially private SQL queries, and DP Auditorium for auditing differential privacy guarantees.
Developers should choose this toolkit if they need to publish statistics from sensitive datasets while providing formal privacy guarantees. The DP building block libraries, Privacy on Beam, and PipelineDP4j are suitable for research, experimental, or production use. Other tools remain experimental and subject to change. The project acknowledges that understanding differential privacy fundamentals is helpful even when using high-level tools, and essential when working with lower-level libraries. For Python users, related projects developed in collaboration with OpenMined provide Python bindings and a Python-native framework.
The project maintains active development across multiple language implementations and tool types, indicating sustained investment in supporting diverse deployment scenarios. Documentation is organized per tool with dedicated README files in respective directories, suggesting a modular maintenance approach. The inclusion of a stochastic testing framework demonstrates attention to correctness and regression prevention in privacy-critical code. The project provides educational resources and acknowledges the learning curve involved in differential privacy, indicating awareness of adoption barriers.
