  Since its conception in 2006, differential privacy has emerged as the
de-facto standard in data privacy, owing to its robust mathematical guarantees,
generalised applicability and rich body of literature. Over the years,
researchers have studied differential privacy and its applicability to an
ever-widening field of topics. Mechanisms have been created to optimise the
process of achieving differential privacy, for various data types and
scenarios. Until this work however, all previous work on differential privacy
has been conducted on a ad-hoc basis, without a single, unifying codebase to
implement results.
  In this work, we present the IBM Differential Privacy Library, a general
purpose, open source library for investigating, experimenting and developing
differential privacy applications in the Python programming language. The
library includes a host of mechanisms, the building blocks of differential
privacy, alongside a number of applications to machine learning and other data
analytics tasks. Simplicity and accessibility has been prioritised in
developing the library, making it suitable to a wide audience of users, from
those using the library for their first investigations in data privacy, to the
privacy experts looking to contribute their own models and mechanisms for
others to use.
