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Hiding in the Crowd: an Analysis of the Effectiveness of Browser ngerprinting at Large Scale
Univ Rennes, INRIA, CNRS, IRISA, Rennes, France. audry, Benoit.
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.ORCID iD: 0000-0002-4015-4640
2018 (English)In: WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WW2018), Association for Computing Machinery (ACM), 2018, p. 309-318Conference paper, Published paper (Refereed)
Abstract [en]

Browser fingerprinting is a stateless technique, which consists in collecting a wide range of data about a device through browser APIs. Past studies have demonstrated that modern devices present so much diversity that fingerprints can be exploited to identify and track users online. With this work, we want to evaluate if browser fingerprinting is still effective at uniquely identifying a large group of users when analyzing millions of fingerprints over a few months. We collected 2,067,942 browser fingerprints from one of the top 15 French websites. The analysis of this novel dataset sheds a new light on the ever-growing browser fingerprinting domain. The key insight is that the percentage of unique fingerprints in our dataset is much lower than what was reported in the past: only 33.6% of fingerprints are unique by opposition to over 80% in previous studies. We show that non-unique fingerprints tend to be fragile. If some features of the fingerprint change, it is very probable that the fingerprint will become unique. We also confirm that the current evolution of web technologies is benefiting users' privacy significantly as the removal of plugins brings down substantively the rate of unique desktop machines.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2018. p. 309-318
Keywords [en]
browser fingerprinting, privacy, software diversity
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-248368DOI: 10.1145/3178876.3186097ISI: 000460379000030OAI: oai:DiVA.org:kth-248368DiVA, id: diva2:1303716
Conference
27th World Wide Web (WWW) Conference, APR 23-27, 2018, APR 23-27, 2018
Note

QC 20190410

Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-04-10Bibliographically approved

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Baudry, Benoit

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf