Web tracking analysis on top websites through OpenWPM analytics enhancements

Abstract Nowadays, websites employ multiple tracking mechanisms to personalize user experiences and deliver targeted advertisements. While cookies remain the dominant tracking tool, recent trends indicate an increasing adoption of stateless fingerprinting based on JavaScript methods. However, existing studies often focus on specific tracking technologies, while detailed analyses that jointly examine multiple stateful and stateless tracking mechanisms remain limited. To address this gap, we enhanced OpenWPM-Data-Analytics to improve the detection of fingerprinting mechanisms in scans performed with OpenWPM, a widely used framework for web privacy measurement studies. Using this enhanced measurement and analysis pipeline, we conducted an empirical analysis of the top 1000 most-visited websites, jointly examining cookies, web storage, web analytics tools, and browser fingerprinting techniques. The results show that stateful tracking methods such as cookies and web storage remain highly prevalent. However, fingerprinting techniques were detected on nearly one third of the analyzed websites, with JavaScript Enumeration and WebGL being the most common mechanisms. This prevalence is higher than that reported in previous automated studies, suggesting a growing reliance on fingerprinting as third-party cookies face increasing restrictions. In addition, web analytics tools were present in half of the websites analyzed. These findings highlight the growing complexity of modern web tracking and the need to improve current detection and mitigation strategies, particularly against fingerprinting mechanisms that are more difficult to block and regulate.