PipelineDP4j is an end-to-end differential privacy solution for JVM that supports various frameworks for distributed data processing such as Apache Beam and Apache Spark (coming soon). It is intended to be usable by all developers, regardless of their differential privacy expertise.
Internally, PipelineDP4j relies on the lower-level building blocks from the differential privacy library and combines them into an "out-of-the-box" solution that takes care of all the steps that are essential to differential privacy, including noise addition, partition selection, and contribution bounding. Thus, rather than using the lower-level differential privacy library, it is recommended to use PipelineDP4j, as it can reduce implementation mistakes.
You can use PipelineDP4j in Java, Kotlin or Scala.
WARNING: Current API version (0.0.1) is experimental and will be changed in 2024 without backward-compatibility. The experimental API won't be supported and maintained after that.
Familiarize yourself with an example. It shows how to compute differentially private statistics on a real dataset using the library.
The public API of the library is located in the API package. You can look at it if you need something beyond the example.
The easiest way to start using the library in your project is to use the dependency from Maven repository. You can find it here. After adding this dependency into your project you can write the same code as in the example above and it will compile.
Please, don't use 0.0.1 version in production code as it is experimental and
its maintenance will be stopped in 2024 with release of the new version.