Privacy-preserving local analysis of digital trace data: A proof-of-concept

Laura Boeschoten*, Adriënne Mendrik, Emiel van der Veen, Jeroen Vloothuis, Haili Hu, Roos Voorvaart, Daniel L. Oberski

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
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Abstract

We present PORT, a software platform for local data extraction and analysis of digital trace data. While digital trace data hold huge potential for social-scientific discovery, their most useful parts have been unattainable for scientists because of privacy concerns and prohibitive access to application programming interfaces. Recently, a workflow was introduced allowing citizens to donate their digital traces to scientists. In this workflow, citizens’ digital traces are processed locally on their machines before providing informed consent to share a subset of the data with 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 outside observers, including the researchers themselves. When using PORT, researchers can tailor the local processing procedure suitable to the data download package and research question. Thus, PORT enables a host of potential applications of social data science to hitherto unobtainable data.

Original languageEnglish
Article number100444
JournalPatterns
Volume3
Issue number3
DOIs
Publication statusPublished - 11 Mar 2022

Keywords

  • data donation
  • data extraction
  • digital trace data
  • DSML 2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem
  • local processing
  • privacy
  • proof-of-concept
  • software

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