Research
Technical profile
My academic background includes computer science (Leiden Institute of Advanced Computer Science). My research is web privacy measurement: crawling websites, recording which third parties receive data, and classifying the tracking and real-time bidding systems behind them. The material below is the technical work that sits behind my casework, expert reports and press quotes.
Thesis
Web Privacy Measurement in Real-Time Bidding Systems. A Graph-Based Approach to RTB System Classification. Doctoral thesis, Leiden University, 2019.
Thesis in the Leiden repository
Earlier work: Web Tracking Detection System , Master's thesis, Leiden Institute of Advanced Computer Science, 2011.
Tools and data
Small, single-purpose public repositories. They are provided as published, most without a formal licence or recent maintenance.
Python script that loads an OpenWPM crawl database (SQLite) into a Neo4j graph for analysis. Written in 2017 during my doctoral research.
Python · no licence specified · published January 2017
Crawl data of Dutch political party websites collected in November 2023, used for the tracking-cookie findings reported by NOS. The repository has no README.
Data (HTML, zip archives) · no licence specified · last updated November 2023
list-tokens (GGUF Token Lister)
Python script that lists tokens from the vocabulary section of a GGUF model file. Used for the Future of Privacy Forum report Technologist Roundtable: Key Issues in AI and Data Protection (2024).
Python · permissive licence notice in the README · last updated November 2024
More at github.com/rvaneijk .
Publications
Scholarly works, including the crawl datasets deposited with DANS, are listed in the portfolio . Author profiles: ORCID | Google Scholar