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

Bahrami, Pouneh Nikkhah; Iqbal, Umar; Shafiq, Zubair

doi:10.2478/popets-2022-0056

Cited by 10 publications

(2 citation statements)

References 44 publications

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“…Unfortunately, there are no definite and readily available labels for Javascript scripts that can be used for fingerprinting detection, and creating them manually and at scale is hard. Therefore, we follow an approach similar to previous work [13,39], using high-precision heuristics to generate binary labels (fingerprinting/non-fingerprinting). While these heuristics have high precision, they are typically narrowly defined to minimize false positives; as a result, they miss fingerprinting scripts.…”

Section: Ground Truthmentioning

confidence: 99%

FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting

Annamalai,

Bilogrevic,

De Cristofaro

2024

Proceedings 2024 Network and Distributed System Security Symposium

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Browser fingerprinting often provides an attractive alternative to third-party cookies for tracking users across the web. In fact, the increasing restrictions on third-party cookies placed by common web browsers and recent regulations like the GDPR may accelerate the transition. To counter browser fingerprinting, previous work proposed several techniques to detect its prevalence and severity. However, these rely on 1) centralized web crawls and/or 2) computationally intensive operations to extract and process signals (e.g., information-flow and static analysis).To address these limitations, we present FP-Fed, the first distributed system for browser fingerprinting detection. Using FP-Fed, users can collaboratively train on-device models based on their real browsing patterns, without sharing their training data with a central entity, by relying on Differentially Private Federated Learning (DP-FL). To demonstrate its feasibility and effectiveness, we evaluate FP-Fed's performance on a set of 18.3k popular websites with different privacy levels, numbers of participants, and features extracted from the scripts. Our experiments show that FP-Fed achieves reasonably high detection performance and can perform both training and inference efficiently, on-device, by only relying on runtime signals extracted from the execution trace, without requiring any resource-intensive operation.

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Section: Ground Truthmentioning

confidence: 99%

FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting

Annamalai,

Bilogrevic,

De Cristofaro

2024

Proceedings 2024 Network and Distributed System Security Symposium

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“…The most relevant work to this paper is FP-Radar [4], a system used to analyze different browsing fingerprinting methods on websites, including mouse fingerprinting. The system looks for the most common mouse events used to collect information.…”

Section: Background and Related Workmentioning

confidence: 99%

Detecting and Analyzing Mouse Tracking in the Wild

Urpí-Bricollé,

Castell-Uroz,

Barlet-Ros

2023

2023 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)

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Nowadays, most websites collect personal information about their users in order to identify them and personalize their services. Among the tools used to that end, fingerprinting is one of the most advanced and precise methods, given the huge amount of features they can collect and combine to build a robust identifier of the user. Although many fingerprinting techniques have recently been studied in the literature, the use and prevalence of mouse tracking, a method that collects information about the computer pointer, is still unexplored in detail. In this work, we propose a new methodology to detect this tracking method and measure its actual usage on the top 80,000 most popular websites. Our results show that about 1.2% of the analyzed websites use some sort of mouse tracking, including some popular websites within the top-1k ranking.

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