[Author Prev][Author Next][Thread Prev][Thread Next][Author Index][Thread Index]
How I Learned to Stop Ph34ring NSA and Love the Base Rate Fallacy
- To: or-talk@xxxxxxxx, or-dev@xxxxxxxx
- Subject: How I Learned to Stop Ph34ring NSA and Love the Base Rate Fallacy
- From: "The23rd Raccoon" <the.raccoon23@xxxxxxxxx>
- Date: Sun, 28 Sep 2008 12:27:11 +0000
- Delivered-to: archiver@xxxxxxxx
- Delivered-to: or-talk-outgoing@xxxxxxxx
- Delivered-to: or-talk@xxxxxxxx
- Delivery-date: Sun, 28 Sep 2008 08:27:21 -0400
- Dkim-signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=gamma; h=domainkey-signature:received:received:message-id:date:from:to :subject:mime-version:content-type:content-transfer-encoding :content-disposition; bh=lKMEoshwyY2oYKKdnwVUaEJzLJCg7uKffnsivEN3SA8=; b=XMekoAA7FShAnD/ZN4DQyVOxmZpxvk6t0V1LYd9zIH4jCQFgx6U02BLDgBZVQzjNlu stfFSgKTCz9q51uDSg9tpZqOyoQCOLSQVYV63IUD8OgkWmMcOPM5IZGP9gXBDidoQ8U0 2qOZ0WVHkRawk8LqkbjyVSAUJ94uT3A9rqlQc=
- Domainkey-signature: a=rsa-sha1; c=nofws; d=gmail.com; s=gamma; h=message-id:date:from:to:subject:mime-version:content-type :content-transfer-encoding:content-disposition; b=Gpb6cUUJN9gxqzgJ1dkNv8dini9nX3CZZodwryXL0X0UFsLhWZmLdL4vnIldfmq2TX bUuaCyrsAc136iHCRFsXzubGvIkuHbrTgaz16tUKWYYpuE7RUdRkszfl+uF5uoPQ6ekZ 4VIxBBDNvwQF3LQcOEhqZD/HiAprjHRtT1lfM=
- Reply-to: or-talk@xxxxxxxxxxxxx
- Sender: owner-or-talk@xxxxxxxxxxxxx
Ladies and gentlemen, I am a Raccoon who has learned to do math.
It used to be the case that on the Internet, nobody knew you were a
Raccoon. But now that Privacy is dead, I've decided to come clean.
I present to you this anonymously authored, non-peer reviewed
communication to do with what you will. Should anyone actually cite
this work in a published paper, I will ask my brethren to leave their
garbage cans unmolested for the rest of their days.
This post performs some basic analysis of the utility of timing
correlation attacks against a moderately used anonymous network,
specifically with respect to the Base Rate Fallacy of Bayesian
statistics. Via that same analysis, it also for the first time begins to
quantify the utility that additional users bring to a low latency
anonymous network in terms of resistance to timing attacks.
You see, one day I was rifling through the local University dumpster
looking for a free meal, and I stumbled upon a pile of discarded
conference proceedings which I decided to peruse while I dined. After
a while, it became apparent that many papers dealing with timing and
correlation attacks completely ignore the Base Rate Fallacy and the
effect of larger user bases and sample sizes on their results.
Worse, while it may be possible that some papers actually DO report
Bayesian Detection Rate probabilities, those that do never specify
this fact, making their work impossible to differentiate from less
appetizing dumpster morsels.
It was enough to get this Raccoon down, and I survive contently on
moldy bread and discarded hot dogs!
Event Notation and Grounding
Most timing attack papers deal with the true positive rate and the
false positive rate of detection. So let's establish some symbols
and formalize these two rates.
M = Two chosen entry and exit streams Match (are the same stream)
~M = Two chosen entry and exit streams Don't Match (are not the same)
C = Packet/stream timing Correlation predicts a pair is matching
~C = Packet/stream timing Correlation predicts a pair is Non-matching
True Positive Rate: P(C|M)
False Negative Rate: P(~C|M) = 1-P(C|M)
True Negative Rate: P(~C|~M) = 1-P(C|~M)
False Positive Rate: P(C|~M)
Bayesian Detection Rate Derivation
Given a purely hypothetical network with 250,000 concurrent users
generating approximately 5000 network-wide concurrent streams and a
99.9% accurate (Equal Error Rate) generic correlation detector, find
the probability that we actually DO have a matching entry+exit pair
given our Correlator says that we do. This is known as the Bayesian
Detection Rate, and is written as P(M|C).
P(M|C) = P(C|M)*P(M)/P(C)
Summing out M to obtain P(C):
P(C) = P(M)*P(C|M) + P(~M)*P(C|~M)
P(M|C) = P(C|M)*P(M)/(P(M)*P(C|M) + P(~M)*P(C|~M))
Example 1: Fully Correlating Global Adversary
This example deals with an adversary trying to correlate EVERYTHING on
a 5000 concurrent stream network at all times. 5000 concurrent streams
means 5000 entry connections and 5000 exit connections (as an
approximation). This gives 5000^2 possible entry-exit pairings
P(M) = (1/5000)^2 = 4E-8
99.9% EER generic correlation detector:
P(C|M) = 0.999
P(C|~M) = 0.001
P(M|C) = 0.999*4E-8/(4E-8*0.999 + (1-4E-8)*0.001)
P(M|C) = .00004 or .004%.
In other words, we expect that for every 25000 times the correlator
predicts a matching pair, only one of those actually is a valid
match. So much for dragnet surveillance.
Example 2: Single Site-targeting Global Adversary
In this example, the adversary is only interested in connections to a
particular site, say wikileaks.org. For this, let's say the adversary
only has one exit stream at a given time to correlate to a given entry
P(M) = 1/5000 = .0002
P(M|C) = 0.999*.0002/(.0002*0.999 + (1-.0002)*0.001)
P(M|C) = .1666 = 16.66%
So this means that we expect the adversary to have 6 suspect users for
every user that leaks a document to wikileaks.org for a 99.9% accurate
correlator. Unlike the dragnet adversary, the targeted adversary is still
pretty effective, especially since they will almost certainly have additional
a-priori information about the users they suspect to have done the leak.
However, if their correlator drops to just 99% EER, however, P(M|C) drops
to 0.0194 or 1.94%. At 90% EER, P(M|C) is 0.0018 or 0.18%. These
provide with expectations of confusion sets of 52 and 556 users respectively.
Example 3: Single Site-targeting "Local" Adversary
To extend this into the capabilities of a local adversary (as opposed
to global), insert a (c/n)^2 factor into P(M) to account for the likelihood
that the adversary will see both sides of a connection, where c is the
percentage of network bandwidth they control, and n is the total network
capacity. Common accepted reasonable values for c/n are on the order
of 0.1, though this may be much higher for IX-level yet not quite fully
global adversaries. Let's go with 0.3.
P(M) = (1/5000)*(0.3)^2 = .000018
P(M|C) = 0.999*.000018/(.000018*0.999 + (1-.000018)*.001)
P(M|C) = 0.0177 = 1.77%
So this means that an adversary with control of 30% of the network
(such as China, the US, or Germany) can expect to go through 57 other
users before coming across a legitimate match predicted by their
timing detector. At 99% EER, this number goes up to 562, and at 90%
EER, all the way up to 6173.
This seems a bit more infeasible, but may still be doable with enough
information or many repeated observations.
So what does this all mean? Well, first of all, it underscores the
importance of being absolutely clear in timing attack research about
exactly what success probabilities are being reported, so we can
better compare both attacks and defenses. In fact, in the opinion of
this humble Raccoon, a large body of work is somewhat suspect for
lack of clarity, both in this and other respects.
Second, it gives us a small glimmer of hope that maybe all is not lost
against IX, National, or ISP level adversaries. Especially those with
only sampled or connection-level resolution, where fine-grained
details on inter-packet timings is not available (as will likely be
the case with EU data retention).
Finally, it also quantifies that we certainly do benefit from a larger
anonymity network not just in terms of nodes, but also in terms of
total concurrent users doing similar things (like short-lived web
traffic). This quantification strongly indicates that we should avoid
splitting the network into segments if we want to gain any additional
utility from growing it further, aside from simply supporting more
users at some fixed level of anonymity.
. "If you only cite a handful of works, either you are doing
something incredibly novel, or you're not nearly as novel as you
. "Good artists imitate, great artists steal."