Privacy preserving local analysis of digital trace data: A proof-of-concept
Laura Boeschoten, Adri\"enne Mendrik, Emiel van der Veen, Jeroen, Vloothuis, Haili Hu, Roos Voorvaart, Daniel Oberski

TL;DR
PORT is a software platform that allows individuals to locally process their digital trace data, enabling privacy-preserving social data analysis by preventing sensitive data exposure to outside parties.
Contribution
The paper introduces PORT, a novel software tool that implements local data processing of digital traces, enhancing privacy in social data science research.
Findings
PORT successfully processes data locally on user devices.
It shields sensitive data from external access during analysis.
Enables new social science applications with privacy protection.
Abstract
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…
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Taxonomy
TopicsPrivacy, Security, and Data Protection · Privacy-Preserving Technologies in Data · Internet Traffic Analysis and Secure E-voting
