Statistical identification of encrypted web browsing traffic

Statistical identification of encrypted web browsing traffic (2002)

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by Qixiang Sun , Daniel R. Simon , Yi-Min Wang , Wilf Russell , Venkata N. Padmanabhan , Lili Qiu

Venue:	IEEE Symposium on Security and Privacy
Citations:	67 - 0 self

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Datum	Value	Source
TITLE	Statistical identification of encrypted web browsing traffic	INFERENCE
AUTHOR NAME	Qixiang Sun	user correction
AUTHOR AFFIL	Microsoft Research; Microsoft Corporation	user correction
AUTHOR ADDR	One Microsoft Way; Redmond, WA 98052	user correction
AUTHOR NAME	Daniel R. Simon	user correction
AUTHOR AFFIL	Microsoft Research; Microsoft Corporation	user correction
AUTHOR ADDR	One Microsoft Way; Redmond, WA 98052	user correction
AUTHOR NAME	Yi-Min Wang	user correction
AUTHOR AFFIL	Microsoft Research; Microsoft Corporation	user correction
AUTHOR ADDR	One Microsoft Way; Redmond, WA 98052	user correction
AUTHOR NAME	Wilf Russell	user correction
AUTHOR AFFIL	Microsoft Research; Microsoft Corporation	user correction
AUTHOR ADDR	One Microsoft Way; Redmond, WA 98052	user correction
AUTHOR NAME	Venkata N. Padmanabhan	user correction
AUTHOR AFFIL	Microsoft Research; Microsoft Corporation	user correction
AUTHOR ADDR	One Microsoft Way; Redmond, WA 98052	user correction
AUTHOR NAME	Lili Qiu	user correction
AUTHOR AFFIL	Microsoft Research; Microsoft Corporation	user correction
AUTHOR ADDR	One Microsoft Way; Redmond, WA 98052	user correction
ABSTRACT	Encryption is often proposed as a tool for protecting the privacy of World Wide Web browsing. However, encryption-particularly as typically implemented in, or in concert with popular Web browsers-does not hide all information about the encrypted plaintext. Specifically, HTTP object count and sizes are often revealed (or at least incompletely concealed). We investigate the identifiability of World Wide Web traffic based on this unconcealed information in a large sample of Web pages, and show that it suffices to identify a significant fraction of them quite reliably. We also suggest some possible countermeasures against the exposure of this kind of information and experimentally evaluate their effiectiveness.	user correction
YEAR	2002	INFERENCE
VENUE	IEEE Symposium on Security and Privacy	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	25 found	ParsCit 1.0