  Website fingerprinting enables an attacker to infer which web page a client
is browsing through encrypted or anonymized network connections. We present a
new website fingerprinting technique based on random decision forests and
evaluate performance over standard web pages as well as Tor hidden services, on
a larger scale than previous works. Our technique, k-fingerprinting, performs
better than current state-of-the-art attacks even against website
fingerprinting defenses, and we show that it is possible to launch a website
fingerprinting attack in the face of a large amount of noisy data. We can
correctly determine which of 30 monitored hidden services a client is visiting
with 85% true positive rate (TPR), a false positive rate (FPR) as low as 0.02%,
from a world size of 100,000 unmonitored web pages. We further show that error
rates vary widely between web resources, and thus some patterns of use will be
predictably more vulnerable to attack than others.
