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Erschienen in:
Automated Analysis of Equivalence Properties for Security Protocols Using Else Branches Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Quantifying Web Adblocker Privacy

verfasst von : Arthur Gervais, Alexandros Filios, Vincent Lenders, Srdjan Capkun

Erschienen in: Computer Security – ESORICS 2017

Verlag: Springer International Publishing

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Abstract

Web advertisements, an integral part of today’s web browsing experience, financially support countless websites. Meaningful advertisements, however, require behavioral targeting, user tracking and profile fingerprinting that raise serious privacy concerns. To counter privacy issues and enhance usability, adblockers emerged as a popular way to filter web requests that do not serve the website’s main content. Despite their popularity, little work has focused on quantifying the privacy provisions of adblockers.
In this paper, we develop a quantitative framework to compare the privacy provisions of adblockers objectively. For our methodology, we introduce several privacy metrics that capture not only the technical web architecture but also the underlying corporate institutions of the problem across time and geography.
Using our framework, we quantify the web privacy implications of 12 ad-blocking software combinations and browser settings on 1000 websites on a daily basis over a timespan of three weeks (a total of 252’000 crawls). Our results highlight a significant difference among adblockers regarding filtering performance, in particular, affected by the applied configurations. Our experimental results confirm that our framework provides consistent results and hence can be used as a quantitative methodology to assess other configurations and adblockers further.

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Vorheriges Kapitel Automated Analysis of Equivalence Properties for Security Protocols Using Else Branches
Nächstes Kapitel More Efficient Structure-Preserving Signatures - Or: Bypassing the Type-III Lower Bounds
Fußnoten
1
Arguably, users also leak private information to first party domains when they visit and interact with those sites, however, since users are visiting these first parties deliberately, the privacy risks are known to the users and controllable without an adblocker.
 
2
User Agent: Mozilla/5.0 (iPhone; CPU iPhone OS 6_0 like Mac OS X) AppleWebKit/536.26 (KHTML, like Gecko) Version/6.0 Mobile/10A5376e Safari/8536.25.
 
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Metadaten
Titel
Quantifying Web Adblocker Privacy
verfasst von
Arthur Gervais
Alexandros Filios
Vincent Lenders
Srdjan Capkun
Copyright-Jahr
2017
Buch
Computer Security – ESORICS 2017

Print ISBN: 978-3-319-66398-2

Electronic ISBN: 978-3-319-66399-9

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-66399-9_2