FP-Radar: Longitudinal Measurement and Early Detection of Browser Fingerprinting

TL;DR

FP-Radar is a machine learning system that analyzes longitudinal web API usage data over a decade to detect both known and emerging browser fingerprinting techniques early, including novel API abuses.

Contribution

It introduces a novel longitudinal measurement approach combined with machine learning to detect evolving and new fingerprinting techniques across a large set of websites.

Findings

Successfully detects abuse of known APIs like WebGL and Sensor.

Identifies previously unknown API abuses such as Gamepad and Clipboard.

First to detect Visibility API abuse for ephemeral fingerprinting in the wild.

Abstract

Browser fingerprinting is a stateless tracking technique that attempts to combine information exposed by multiple different web APIs to create a unique identifier for tracking users across the web. Over the last decade, trackers have abused several existing and newly proposed web APIs to further enhance the browser fingerprint. Existing approaches are limited to detecting a specific fingerprinting technique(s) at a particular point in time. Thus, they are unable to systematically detect novel fingerprinting techniques that abuse different web APIs. In this paper, we propose FP-Radar, a machine learning approach that leverages longitudinal measurements of web API usage on top-100K websites over the last decade, for early detection of new and evolving browser fingerprinting techniques. The results show that FP-Radar is able to early detect the abuse of newly introduced properties of already known (e.g., WebGL, Sensor) and as well as previously unknown (e.g., Gamepad, Clipboard) APIs for browser fingerprinting. To the best of our knowledge, FP-Radar is also the first to detect the abuse of the Visibility API for ephemeral fingerprinting in the wild.