Research
I trained in computer science at the Leiden Institute of Advanced Computer Science and earned my PhD at Leiden Law School. 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. This is the technical work behind my casework, expert reports, and press quotes.
Theses
Doctoral thesis, Faculty of Law, Leiden University, defended 29 January 2019: Web Privacy Measurement in Real-Time Bidding Systems: A Graph-Based Approach to RTB System Classification
Researched at eLaw (Center for Law and Digital Technologies) and the Dual PhD Centre, alongside my work at the Dutch DPA.
SIKS Dissertation Series 2019-01; ISBN 978-94-028-1323-4; CC BY-NC-SA 4.0
Thesis in the Leiden repository
Earlier: Master's thesis Web Tracking Detection System (Leiden Institute of Advanced Computer Science, 2011).
Tools and data
Single-purpose tools and datasets behind published findings.
Loads an OpenWPM crawl database (SQLite) into a Neo4j graph for analysis. Written during my doctoral research.
- Python
- 2017
Crawl data of Dutch political party websites, collected in November 2023 and used for the tracking-cookie findings reported by NOS.
- Crawl data (HTML)
- 2023
list-tokens (GGUF Token Lister)
Lists the tokens in 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
- 2024
More on github.com/rvaneijk
Publications
The portfolio lists all scholarly works, including the crawl datasets deposited with DANS. Author profiles: ORCID Google Scholar