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Actions speak louder than words: Semi-supervised learning for browser fingerprinting detection

Sarah Bird 1 Vikas Mishra 2 Steven Englehardt 1 Rob Willoughby 1 David Zeber 1 Walter Rudametkin 2 Martin Lopatka 1 
2 SPIRALS - Self-adaptation for distributed services and large software systems
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : As online tracking continues to grow, existing anti-tracking and fingerprinting detection techniques that require significant manual input must be augmented. Heuristic approaches to fingerprinting detection are precise but must be carefully curated. Supervised machine learning techniques proposed for detecting tracking require manually generated label-sets. Seeking to overcome these challenges, we present a semi-supervised machine learning approach for detecting fingerprinting scripts. Our approach is based on the core insight that fingerprinting scripts have similar patterns of API access when generating their fingerprints, even though their access patterns may not match exactly. Using this insight, we group scripts by their JavaScript (JS) execution traces and apply a semi-supervised approach to detect new fingerprinting scripts. We detail our methodology and demonstrate its ability to identify the majority of scripts ($\geqslant$94.9%) identified by existing heuristic techniques. We also show that the approach expands beyond detecting known scripts by surfacing candidate scripts that are likely to include fingerprinting. Through an analysis of these candidate scripts we discovered fingerprinting scripts that were missed by heuristics and for which there are no heuristics. In particular, we identified over one hundred device-class fingerprinting scripts present on hundreds of domains. To the best of our knowledge, this is the first time device-class fingerprinting has been measured in the wild. These successes illustrate the power of a sparse vector representation and semi-supervised learning to complement and extend existing tracking detection techniques.
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Preprints, Working Papers, ...
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https://hal.inria.fr/hal-03297181
Contributor : Vikas Mishra Connect in order to contact the contributor
Submitted on : Friday, July 23, 2021 - 11:28:31 AM
Last modification on : Thursday, March 31, 2022 - 4:37:57 AM

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  • HAL Id : hal-03297181, version 1
  • ARXIV : 2003.04463

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Sarah Bird, Vikas Mishra, Steven Englehardt, Rob Willoughby, David Zeber, et al.. Actions speak louder than words: Semi-supervised learning for browser fingerprinting detection. 2021. ⟨hal-03297181⟩

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