An iterative technique to identify browser fingerprinting scripts
This work addresses the problem of privacy invasion for web users by improving detection of stealthy browser fingerprinting, though it is incremental as it builds on existing classification methods.
The paper tackles the challenge of detecting browser fingerprinting scripts by proposing a new detection technique that combines automatic API call similarity matching with manual classification steps, and they publicly share their algorithm and implementation to aid reproducibility.
Browser fingerprinting is a stateless identification technique based on browser properties. Together, they form an identifier that can be collected without users' notice and has been studied to be unique and stable. As this technique relies on browser properties that serve legitimate purposes, the detection of this technique is challenging. While several studies propose classification techniques, none of these are publicly available, making them difficult to reproduce. This paper proposes a new browser fingerprinting detection technique. Based on an incremental process, it relies on both automatic and manual decisions to be both reliable and fast. The automatic step matches API calls similarities between scripts while the manual step is required to classify a script with different calls. We publicly share our algorithm and implementation to improve the general knowledge on the subject.