Security News > 2020 > June > IBM Research releases differential privacy library that works with machine learning
The library "Boasts a suite of tools for machine learning and data analytics tasks, all with built-in privacy guarantees," according to Naoise Holohan, a research staff member on IBM Research Europe's privacy and security team.
Differential privacy allows data collectors to use mathematical noise to anonymize information, and IBM's library is special because it's machine learning functionality enables organizations to publish and share their data with rigorous guarantees on user privacy.
"Originally, when we started looking at the space of open-source software and differential privacy, we noticed that there was a big gap in the market in terms of being able to do machine learning with differential privacy easily. There is a lot of work done in the literature that all the algorithms have been studied and made differentially private and solutions have been presented but there was no single repository or single library to go to do machine learning with differential privacy," he said.
Holohan said the IBM repository is already being used extensively for experimentation and to see what effect differential privacy has on machine learning algorithms.
Academic institutions and bloggers are using the software to show how differential privacy works and he added that the library is being used internally at IBM to look at the impact of differential privacy on various applications.