An empirical study of privacy-violating information flows in JavaScript web applications
By Dongseok Jang, Ranjit Jhala, Sorin Lerner, Hovav Shacham. 2010
209 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Giovanni Vigna
- Christopher Krügel
- Nick Nikiforakis
- David Wetherall
- Claudia Díaz
- Bart Preneel
- Frank Piessens
- Wouter Joosen
In Information Systems
- Giovanni Vigna
- Christopher Krügel
- David Wetherall
- Bart Preneel
- Frank Piessens
- Wouter Joosen
- Tadayoshi Kohno
- John C. Mitchell
In Signal Processing
- Giovanni Vigna
- Christopher Krügel
- Claudia Díaz
- Bart Preneel
- Frank Piessens
- Tadayoshi Kohno
- John C. Mitchell
- Marc Juárez
Citations in the corpus
- Cookieless Monster: Exploring the Ecosystem of Web-Based Device Fingerprinting (2013, background): “While a browser does not directly provide that list, one can acquire it using either a browser plugin that willingly provides this information or using a side-channel that indirectly reveals the presence or absence of any given font.”
- Detecting and Defending Against Third-Party Tracking on the Web (2012, background): “From a slightly different threat model, the authors of [11] examined privacy-violating information flows on the web, though they don’t distinguish third-party trackers from visited sites themselves.”
- FPDetective: dusting the web for fingerprinters (2013, background): “Furthermore, a user’s browsing history, which can be recovered exploiting JavaScript’s visited-link color feature [9], has also been shown to uniquely identify users [19].”
- Third-Party Web Tracking: Policy and Technology (2012, background): “Much of the discussion is based on recent results from a new dynamic web measurement platform, FourthParty.”