Differential Privacy: A New Approach to Dealing with Social Big Data

Differential privacy is a formal property that data models should fulfill in order to allow valid statistical conclusions without impacting the privacy of individuals. The technique involves adding noise to datasets in a way that makes it impossible to reverse-engineer the results of the analysis in order to get the individual sensitive data inputs.

While the theoretical side of differential privacy is a widely researched area, the actual application of differential privacy in industry and society has so far been very limited. The goal of the project is to research the practical application of differential privacy.

Some of the research questions are:

- What are the opportunities of differential privacy from a technical and societal perspective?
- What are the risks of differential privacy for users and society?
- How can differential privacy be taught in an accessible and applicable way?
- How can differential privacy be implemented in compliance with the GDPR?