#DataForGood in theory - Privacy preserving machine learning

In our March call one of our newest members, Andreas Wieltsch, will speak about "Privacy preserving machine learning". Recently, new privacy preserving techniques for machine learning have gained momentum in both research and practical application. Andreas will present concepts and methods that enable computations on encrypted data and model-building on distributed devices without sharing data.

Further we will discuss how our projects are going and how you can join our #DataForGood activities.

Who are we?
We are CorrelAid, a network of more than 1500 data scientists who want to change the world with data science (correlaid.org). We are helping organizations that do good with data science projects.

This CorrelAid virtual Meetup is open to everyone who is interested in what we do: you will also get a short introduction into CorrelAid, and some use case examples of how data science can be used to do good.