  We present PORT, a software platform for local data extraction and analysis
of digital trace data. While digital trace data collected by private and public
parties hold a huge potential for social-scientific discovery, their most
useful parts have been unattainable for academic researchers due to privacy
concerns and prohibitive API access. However, the EU General Data Protection
Regulation (GDPR) grants all citizens the right to an electronic copy of their
personal data. All major data controllers, such as social media platforms,
banks, online shops, loyalty card systems and public transportation cards
comply with this right by providing their clients with a `Data Download
Package' (DDP). Previously, a conceptual workflow was introduced allowing
citizens to donate their data to scientific- researchers. In this workflow,
citizens' DDPs are processed locally on their machines before they are asked to
provide informed consent to share a subset of the processed data with the
researchers. In this paper, we present the newly developed software PORT that
implements the local processing part of this workflow, protecting privacy by
shielding sensitive data from any contact with outside observers -- including
the researchers themselves. Thus, PORT enables a host of potential applications
of social data science to hitherto unobtainable data.

Cryptography and Security