Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-31284-7_25
Title: Tracking the trackers: Fast and scalable dynamic analysis of web content for privacy violations
Authors: Tran, M.
Dong, X.
Liang, Z. 
Jiang, X.
Keywords: Dynamic analysis
Information flow
JavaScript
Privacy
Web security
Issue Date: 2012
Source: Tran, M.,Dong, X.,Liang, Z.,Jiang, X. (2012). Tracking the trackers: Fast and scalable dynamic analysis of web content for privacy violations. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7341 LNCS : 418-435. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-31284-7_25
Abstract: JavaScript-based applications are very popular on the web today. However, the lack of effective protection makes various kinds of privacy violation attack possible, including cookie stealing, history sniffing and behavior tracking. There have been studies of the prevalence of such attacks, but the dynamic nature of the JavaScript language makes reasoning about the information flows in a web application a challenging task. Previous small-scale studies do not present a complete picture of privacy violations of today's web, especially in the context of Internet advertisements and web analytics. In this paper we present a novel, fast and scalable architecture to address the shortcomings of previous work. Specifically, we have developed a novel technique called principal-based tainting that allows us to perform dynamic analysis of JavaScript execution with lowered performance overhead. We have crawled and measured more than one million websites. Our findings show that privacy attacks are more prevalent and serious than previously known. © 2012 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/41493
ISBN: 9783642312830
ISSN: 03029743
DOI: 10.1007/978-3-642-31284-7_25
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