Diffprivlib: The IBM Differential Privacy Library

TL;DR

The paper introduces Diffprivlib, an open-source Python library that unifies and simplifies the implementation of differential privacy mechanisms and applications for researchers and practitioners.

Contribution

It presents a comprehensive, accessible, and open-source library for differential privacy, integrating various mechanisms and applications in a single Python package.

Findings

Provides a unified codebase for differential privacy mechanisms

Includes applications to machine learning and data analytics

Designed for ease of use for both novices and experts

Abstract

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.