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SUMMARY:Bayes\, not Naive: Security Bounds on Website Fingerprinting Defen
 ses - Giovanni Cherubin\, Information Security Group (ISG)\, Royal Hollowa
 y\, University of London
DTSTART:20171128T140000Z
DTEND:20171128T150000Z
UID:TALK76351@talks.cam.ac.uk
CONTACT:Alexander Vetterl
DESCRIPTION:Website Fingerprinting attacks allow an adversary to predict w
 hich web pages a victim visits\, even when she browses through Tor/VPN\, b
 y using Machine Learning classification techniques on the encrypted traffi
 c she produces. To date\, the standard method for evaluating Website Finge
 rprinting defences is testing them against state-of-the-art attacks\; this
  generated a 10 years-long arms race.    \n\nThis talk presents a practica
 l method for deriving security bounds for Website Fingerprinting defences\
 , which is based on an original application of Machine Learning theory. Th
 e method gives\, with respect to the set of features used by an adversary\
 , a lower bound estimate of the smallest error the adversary can achieve\,
  for any classifier he may use. This result i) allows practitioners to eva
 luate and compare defences in terms of their security\, and ii) it favours
  the shift of WF research to a classifier-agnostic identification of optim
 al features.
LOCATION:LT2\, Computer Laboratory\, William Gates Building
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