[tor-commits] [torspec/master] Write a proposal for the mesh vanguards design.
nickm at torproject.org
nickm at torproject.org
Sun May 27 13:59:29 UTC 2018
commit d4aaf28eb3a3ae9f60996d97ed6bf21153cc7601
Author: Mike Perry <mikeperry-git at torproject.org>
Date: Tue May 8 21:14:43 2018 +0000
Write a proposal for the mesh vanguards design.
---
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+Title: Mesh-based vanguards
+Authors: George Kadianakis and Mike Perry
+Created: 2018-05-08
+Status: Draft
+
+0. Motivation
+
+ A guard discovery attack allows attackers to determine the guard
+ node of a Tor client. The hidden service rendezvous protocol
+ provides an attack vector for a guard discovery attack since anyone
+ can force an HS to construct a 3-hop circuit to a relay (#9001).
+
+ Following the guard discovery attack with a compromise and/or
+ coercion of the guard node can lead to the deanonymization of a
+ hidden service.
+
+1. Overview
+
+ This document tries to make the above guard discovery + compromise
+ attack harder to launch. It introduces a configuration
+ option which makes the hidden service also pin the second and third
+ hops of its circuits for a longer duration.
+
+ With this new path selection, we force the adversary to perform a
+ Sybil attack and two compromise attacks before succeeding. This is
+ an improvement over the current state where the Sybil attack is
+ trivial to pull off, and only a single compromise attack is required.
+
+ With this new path selection, an attacker is forced to do a one or
+ more node compromise attacks before learning the guard node of a hidden
+ service. This increases the uncertainty of the attacker, since
+ compromise attacks are costly and potentially detectable, so an
+ attacker will have to think twice before beginning a chain of node
+ compromise attacks that they might not be able to complete.
+
+1.1. Tor integration
+
+ The mechanisms introduced in this proposal are currently implemented
+ partially in Tor and partially through an external Python script:
+ https://github.com/mikeperry-tor/vanguards
+
+ The Python script uses the new Tor configuration options HSLayer2Nodes and
+ HSLayer3Nodes to be able to select nodes for the guard layers. The Python
+ script is tasked with maintaining and rotating the guard nodes as needed
+ based on the lifetimes described in this proposal.
+
+ In the future, we are aiming to include the whole functionality into Tor,
+ with no need for external scripts.
+
+1.2. Visuals
+
+ Here is how a hidden service rendezvous circuit currently looks like:
+
+ -> middle_1 -> middle_A
+ -> middle_2 -> middle_B
+ -> middle_3 -> middle_C
+ -> middle_4 -> middle_D
+ HS -> guard -> middle_5 -> middle_E
+ -> middle_6 -> middle_F
+ -> middle_7 -> middle_G
+ -> middle_8 -> middle_H
+ -> ... -> ...
+ -> middle_n -> middle_n
+
+ this proposal pins the two middle positions into a much more
+ restricted sets, as follows:
+
+ -> guard_2A
+ -> guard_3A
+ -> guard_1A -> guard_2B -> guard_3B
+ HS -> guard_3C
+ -> guard_1B -> guard_2C -> guard_3D
+ -> guard_3E
+ -> guard_2D -> guard_3F
+
+ Additionally, to avoid linkability, we insert an extra middle node
+ after the third layer guard for client side intro and hsdir circuits,
+ and service-side rendezvous circuits. This means that the set of
+ paths for Client (C) and Service (S) side look like this:
+
+ C - G - L2 - L3 - R
+ S - G - L2 - L3 - HSDIR
+ S - G - L2 - L3 - I
+ C - G - L2 - L3 - M - I
+ C - G - L2 - L3 - M - HSDIR
+ S - G - L2 - L3 - M - R
+
+1.3. Threat model, Assumptions, and Goals
+
+ Consider an adversary with the following powers:
+
+ - Can launch a Sybil guard discovery attack against any node of a
+ rendezvous circuit. The slower the rotation period of the node,
+ the longer the attack takes. Similarly, the higher the percentage
+ of the network is compromised, the faster the attack runs.
+
+ - Can compromise any node on the network, but this compromise takes
+ time and potentially even coercive action, and also carries risk
+ of discovery.
+
+ We also make the following assumptions about the types of attacks:
+
+ 1. A Sybil attack is observable by both people monitoring the network
+ for large numbers of new nodes, as well as vigilant hidden service
+ operators. It will require either large amounts of traffic sent
+ towards the hidden service, multiple test circuits, or both.
+
+ 2. A Sybil attack against the second or first layer Guards will be
+ more noisy than a Sybil attack against the third layer guard, since the
+ second and first layer Sybil attack requires a timing side channel in
+ order to determine success, whereas the Sybil success is almost
+ immediately obvious to third layer guard, since it will be instructed
+ to connect to a cooperating malicious rend point by the adversary.
+
+ 3. As soon as the adversary is confident they have won the Sybil attack,
+ an even more aggressive circuit building attack will allow them to
+ determine the next node very fast (an hour or less).
+
+ 4. The adversary is strongly disincentivized from compromising nodes that
+ may prove useless, as node compromise is even more risky for the
+ adversary than a Sybil attack in terms of being noticed.
+
+ Given this threat model, our security parameters were selected so that
+ the first two layers of guards should be hard to attack using a Sybil
+ guard discovery attack and hence require a node compromise attack. Ideally,
+ we want the node compromise attacks to carry a non-negligible probability of
+ being useless to the adversary by the time they complete.
+
+ On the other hand, the outermost layer of guards should rotate fast enough to
+ _require_ a Sybil attack.
+
+ See our vanguard simulator project for a simulation of the above adversary
+ model and a motivation for the parameters selected within this proposal:
+ https://github.com/asn-d6/vanguard_simulator
+ https://github.com/asn-d6/vanguard_simulator/wiki/Optimizing-vanguard-topologies
+
+
+2. Design
+
+ When a hidden service picks its guard nodes, it also picks an
+ additional NUM_LAYER2_GUARDS-sized set of middle nodes for its
+ `second_guard_set`, as well as a NUM_LAYER3_GUARDS-sized set of
+ middle nodes for its `third_guard_set`.
+
+ When a hidden service needs to establish a circuit to an HSDir,
+ introduction point or a rendezvous point, it uses nodes from
+ `second_guard_set` as the second hop of the circuit and nodes from
+ `third_guard_set` as third hop of the circuit.
+
+ A hidden service rotates nodes from the 'second_guard_set' at a random
+ time between MIN_SECOND_GUARD_LIFETIME hours and
+ MAX_SECOND_GUARD_LIFETIME hours.
+
+ A hidden service rotates nodes from the 'third_guard_set' at a random
+ time between MIN_THIRD_GUARD_LIFETIME and MAX_THIRD_GUARD_LIFETIME
+ hours.
+
+ Each node's rotation time is tracked independently, to avoid disclosing
+ the rotation times of the primary and second-level guards.
+
+2.1. Security parameters
+
+ We set NUM_LAYER2_GUARDS to 4 nodes and NUM_LAYER3_GUARDS to 6 nodes.
+
+ We set MIN_SECOND_GUARD_LIFETIME to 1 day, and MAX_SECOND_GUARD_LIFETIME
+ to 45 days inclusive, for an average rotation rate of 29.5 days, using
+ the max(X,X) distribution specified in Section 3.3.
+
+ We set MIN_THIRD_GUARD_LIFETIME to 1 hour, and MAX_THIRD_GUARD_LIFETIME
+ to 48 hours inclusive, for an average rotation rate of 31.5 hours, using
+ the max(X,X) distribution specified in Section 3.3.
+
+ See Section 3 for more analysis on these constants.
+
+2.2. Path restriction changes
+
+ In order to avoid information leaks and ensure paths can be built, path
+ restrictions must be loosened.
+
+ In particular, we allow the following:
+ 1. Nodes from the same /16 and same family for any/all hops
+ 2. Guard nodes can be chosen for RP/IP/HSDIR
+ 3. Guard nodes can be chosen for hop before RP/IP/HSDIR.
+
+ The first change prevents the situation where paths cannot be built if two
+ layers all share the same subnet and/or node family. It also prevents the
+ the use of a different entry guard based on the family or subnet of the
+ IP, HSDIR, or RP.
+
+ The second change prevents an adversary from forcing the use of a different
+ entry guard by enumerating all guard-flaged nodes as the RP.
+
+ The third change prevents an adversary from learning the guard node by way
+ of noticing which nodes were not chosen for the hop before it.
+
+
+3. Rationale and Security Parameter Selection
+
+3.1. Sybil rotation counts for a given number of Guards
+
+ The probability of Sybil success for Guard discovery can be modeled as
+ the probability of choosing 1 or more malicious middle nodes for a
+ sensitive circuit over some period of time.
+
+ P(At least 1 bad middle) = 1 - P(All Good Middles)
+ = 1 - P(One Good middle)^(num_middles)
+ = 1 - (1 - c/n)^(num_middles)
+
+ c/n is the adversary compromise percentage
+
+ In the case of Vanguards, num_middles is the number of Guards you rotate
+ through in a given time period. This is a function of the number of vanguards
+ in that position (v), as well as the number of rotations (r).
+
+ P(At least one bad middle) = 1 - (1 - c/n)^(v*r)
+
+ Here's detailed tables in terms of the number of rotations required for
+ a given Sybil success rate for certain number of guards.
+
+ 1.0% Network Compromise:
+ Sybil Success One Two Three Four Five Six Eight Nine Ten Twelve Sixteen
+ 10% 11 6 4 3 3 2 2 2 2 1 1
+ 15% 17 9 6 5 4 3 3 2 2 2 2
+ 25% 29 15 10 8 6 5 4 4 3 3 2
+ 50% 69 35 23 18 14 12 9 8 7 6 5
+ 60% 92 46 31 23 19 16 12 11 10 8 6
+ 75% 138 69 46 35 28 23 18 16 14 12 9
+ 85% 189 95 63 48 38 32 24 21 19 16 12
+ 90% 230 115 77 58 46 39 29 26 23 20 15
+ 95% 299 150 100 75 60 50 38 34 30 25 19
+ 99% 459 230 153 115 92 77 58 51 46 39 29
+
+ 5.0% Network Compromise:
+ Sybil Success One Two Three Four Five Six Eight Nine Ten Twelve Sixteen
+ 10% 3 2 1 1 1 1 1 1 1 1 1
+ 15% 4 2 2 1 1 1 1 1 1 1 1
+ 25% 6 3 2 2 2 1 1 1 1 1 1
+ 50% 14 7 5 4 3 3 2 2 2 2 1
+ 60% 18 9 6 5 4 3 3 2 2 2 2
+ 75% 28 14 10 7 6 5 4 4 3 3 2
+ 85% 37 19 13 10 8 7 5 5 4 4 3
+ 90% 45 23 15 12 9 8 6 5 5 4 3
+ 95% 59 30 20 15 12 10 8 7 6 5 4
+ 99% 90 45 30 23 18 15 12 10 9 8 6
+
+ 10.0% Network Compromise:
+ Sybil Success One Two Three Four Five Six Eight Nine Ten Twelve Sixteen
+ 10% 2 1 1 1 1 1 1 1 1 1 1
+ 15% 2 1 1 1 1 1 1 1 1 1 1
+ 25% 3 2 1 1 1 1 1 1 1 1 1
+ 50% 7 4 3 2 2 2 1 1 1 1 1
+ 60% 9 5 3 3 2 2 2 1 1 1 1
+ 75% 14 7 5 4 3 3 2 2 2 2 1
+ 85% 19 10 7 5 4 4 3 3 2 2 2
+ 90% 22 11 8 6 5 4 3 3 3 2 2
+ 95% 29 15 10 8 6 5 4 4 3 3 2
+ 99% 44 22 15 11 9 8 6 5 5 4 3
+
+ The rotation counts in these tables were generated with:
+ def num_rotations(c, v, success):
+ r = 0
+ while 1-math.pow((1-c), v*r) < success: r += 1
+ return r
+
+3.2. Rotation Period
+
+ As specified in Section 1.2, the primary driving force for the third
+ layer selection was to ensure that these nodes rotate fast enough that
+ it is not worth trying to compromise them, because it is unlikely for
+ compromise to succeed and yield useful information before the nodes stop
+ being used.
+
+ From the table in Section 3.1, with NUM_LAYER2_GUARDS=4 and
+ NUM_LAYER3_GUARDS=6, it can be seen that this means that the Sybil attack
+ on layer3 will complete with 50% chance in 12*31.5 hours (15.75 days)
+ for the 1% adversary, ~4 days for the 5% adversary, and 2.62 days for the
+ 10% adversary.
+
+ Since rotation of each node happens independently, the distribution of
+ when the adversary expects to win this Sybil attack in order to discover
+ the next node up is uniform. This means that on average, the adversary
+ should expect that half of the rotation period of the next node is already
+ over by the time that they win the Sybil.
+
+ With this fact, we choose our range and distribution for the second
+ layer rotation to be short enough to cause the adversary to risk
+ compromising nodes that are useless, yet long enough to require a
+ Sybil attack to be noticeable in terms of client activity. For this
+ reason, we choose a minimum second-layer guard lifetime of 1 day,
+ since this gives the adversary a minimum expected value of 12 hours for
+ during which they can compromise a guard before it might be rotated.
+ If the total expected rotation rate is 29.5 days, then the adversary can
+ expect overall to have 14.75 days remaining after completing their Sybil
+ attack before a second-layer guard rotates away.
+
+3.3. Rotation distributions
+
+ In order to skew the distribution of the third layer guard towards
+ higher values, we use max(X,X) for the distribution, where X is a
+ random variable that takes on values from the uniform distribution.
+
+ Here's a table of expectation (arithmetic means) for relevant
+ ranges of X (sampled from 0..N-1). The table was generated with the
+ following python functions:
+
+ def ProbMinXX(N, i): return (2.0*(N-i)-1)/(N*N)
+ def ProbMaxXX(N, i): return (2.0*i+1)/(N*N)
+
+ def ExpFn(N, ProbFunc):
+ exp = 0.0
+ for i in xrange(N): exp += i*ProbFunc(N, i)
+ return exp
+
+ The current choice for second-layer guards is noted with **, and
+ the current choice for third-layer guards is noted with ***.
+
+ Range Min(X,X) Max(X,X)
+ 40 12.84 26.16
+ 41 13.17 26.83
+ 42 13.50 27.50
+ 43 13.84 28.16
+ 44 14.17 28.83
+ 45 14.50 29.50**
+ 46 14.84 30.16
+ 47 15.17 30.83
+ 48 15.50 31.50***
+
+ The Cumulative Density Function (CDF) tells us the probability that a
+ guard will no longer be in use after a given number of time units have
+ passed.
+
+ Because the Sybil attack on the third node is expected to complete at any
+ point in the second node's rotation period with uniform probability, if we
+ want to know the probability that a second-level Guard node will still be in
+ use after t days, we first need to compute the probability distribution of
+ the rotation duration of the second-level guard at a uniformly random point
+ in time. Let's call this P(R=r).
+
+ For P(R=r), the probability of the rotation duration depends on the selection
+ probability of a rotation duration, and the fraction of total time that
+ rotation is likely to be in use. This can be written as:
+
+ P(R=r) = ProbMaxXX(X=r)*r / \sum_{i=1}^N ProbMaxXX(X=i)*i
+
+ or in Python:
+
+ def ProbR(N, r, ProbFunc=ProbMaxXX):
+ return ProbFunc(N, r)*r/ExpFn(N, ProbFunc)
+
+ For the full CDF, we simply sum up the fractional probability density for
+ all rotation durations. For rotation durations less than t days, we add the
+ entire probability mass for that period to the density function. For
+ durations d greater than t days, we take the fraction of that rotation
+ period's selection probability and multiply it by t/d and add it to the
+ density. In other words:
+
+ def FullCDF(N, t, ProbFunc=ProbR):
+ density = 0.0
+ for d in xrange(N):
+ if t >= d: density += ProbFunc(N, d)
+ # The +1's below compensate for 0-indexed arrays:
+ else: density += ProbFunc(N, d)*(float(t+1))/(d+1)
+ return density
+
+ Computing this yields the following distribution for our current parameters:
+
+ t P(SECOND_ROTATION <= t)
+ 1 0.03247
+ 2 0.06494
+ 3 0.09738
+ 4 0.12977
+ 5 0.16207
+ 10 0.32111
+ 15 0.47298
+ 20 0.61353
+ 25 0.73856
+ 30 0.84391
+ 35 0.92539
+ 40 0.97882
+ 45 1.00000
+
+ This CDF tells us that for the second-level Guard rotation, the
+ adversary can expect that 3.3% of the time, their third-level Sybil
+ attack will provide them with a second-level guard node that has only
+ 1 day remaining before it rotates. 6.5% of the time, there will
+ be only 2 day or less remaining, and 9.7% of the time, 3 days or less.
+
+ Note that this distribution is still a day-resolution approximation.
+
+
+4. Security concerns and mitigations
+
+4.1. Mitigating fingerprinting of new HS circuits
+
+ By pinning the middle nodes of rendezvous circuits, we make it
+ easier for all hops of the circuit to detect that they are part of a
+ special hidden service circuit with varying degrees of certainty.
+
+ The Guard node is able to recognize a Vanguard client with a high
+ degree of certainty because it will observe a client IP creating the
+ overwhelming majority of its circuits to just a few middle nodes in
+ any given 31.5 day time period.
+
+ The middle nodes will be able to tell with a variable certainty that
+ depends on both its traffic volume and upon the popularity of the
+ service, because they will see a large number of circuits that tend to
+ pick the same Guard and Exit.
+
+ The final nodes will be able to tell with a similar level of certainty
+ that depends on their capacity and the service popularity, because they
+ will see a lot of handshakes that all tend to have the same second
+ hops.
+
+ The most serious of these is the Guard fingerprinting issue. When
+ proposal 254-padding-negotiation is implemented, services that enable
+ this feature should use those padding primitives to create fake circuits
+ to random middle nodes that are not their guards, in an attempt to look
+ more like a client.
+
+ Additionally, if Tor Browser implements "virtual circuits" based on
+ SOCKS username+password isolation in order to enforce the re-use of
+ paths when SOCKS username+passwords are re-used, then the number of
+ middle nodes in use during a typical user's browsing session will be
+ proportional to the number of sites they are viewing at any one time.
+ This is likely to be much lower than one new middle node every ten
+ minutes, and for some users, may be close to the number of Vanguards
+ we're considering.
+
+ This same reasoning is also an argument for increasing the number of
+ second-level guards beyond just two, as it will spread the hidden
+ service's traffic over a wider set of middle nodes, making it both
+ easier to cover, and behave closer to a client using SOCKS virtual
+ circuit isolation.
+
+5. Default vs optional behavior
+
+ We suggest this torrc option to be optional because it changes path
+ selection in a way that may seriously impact hidden service performance,
+ especially for high traffic services that happen to pick slow guard
+ nodes.
+
+ However, by having this setting be disabled by default, we make hidden
+ services who use it stand out a lot. For this reason, we should in fact
+ enable this feature globally, but only after we verify its viability for
+ high-traffic hidden services, and ensure that it is free of second-order
+ load balancing effects.
+
+ Even after that point, until Single Onion Services are implemented,
+ there will likely still be classes of very high traffic hidden services
+ for whom some degree of location anonymity is desired, but for which
+ performance is much more important than the benefit of Vanguards, so there
+ should always remain a way to turn this option off.
+
+ In the meantime, a reference implementation is available at:
+ https://github.com/mikeperry-tor/vanguards/blob/master/vanguards/vanguards.py
+
+
+Appendix A: Full Python program for generating tables in this proposal
+
+#!/usr/bin/python
+import math
+
+############ Section 3.1 #################
+def num_rotations(c, v, success):
+ i = 0
+ while 1-math.pow((1-c), v*i) < success: i += 1
+ return i
+
+def rotation_line(c, pct):
+ print " %2d%% %6d%6d%6d%6d%6d%6d%6d%6d%6d%6d%8d" % \
+ (pct, num_rotations(c, 1, pct/100.0), num_rotations(c, 2, pct/100.0), \
+ num_rotations(c, 3, pct/100.0), num_rotations(c, 4, pct/100.0),
+ num_rotations(c, 5, pct/100.0), num_rotations(c, 6, pct/100.0),
+ num_rotations(c, 8, pct/100.0), num_rotations(c, 9, pct/100.0),
+ num_rotations(c, 10, pct/100.0), num_rotations(c, 12, pct/100.0),
+ num_rotations(c, 16, pct/100.0))
+
+def rotation_table_31():
+ for c in [1,5,10]:
+ print "\n %2.1f%% Network Compromise: " % c
+ print " Sybil Success One Two Three Four Five Six Eight Nine Ten Twelve Sixteen"
+ for success in [10,15,25,50,60,75,85,90,95,99]:
+ rotation_line(c/100.0, success)
+
+############ Section 3.3 #################
+def ProbMinXX(N, i): return (2.0*(N-i)-1)/(N*N)
+def ProbMaxXX(N, i): return (2.0*i+1)/(N*N)
+
+def ExpFn(N, ProbFunc):
+ exp = 0.0
+ for i in xrange(N): exp += i*ProbFunc(N, i)
+ return exp
+
+def ProbUniformX(N, i): return 1.0/N
+
+def ProbR(N, r, ProbFunc=ProbMaxXX):
+ return ProbFunc(N, r)*r/ExpFn(N, ProbFunc)
+
+def FullCDF(N, t, ProbFunc=ProbR):
+ density = 0.0
+ for d in xrange(N):
+ if t >= d: density += ProbFunc(N, d)
+ # The +1's below compensate for 0-indexed arrays:
+ else: density += ProbFunc(N, d)*float(t+1)/(d+1)
+ return density
+
+def expectation_table_33():
+ print "\n Range Min(X,X) Max(X,X)"
+ for i in xrange(10,49):
+ print " %2d %2.2f %2.2f" % (i, ExpFn(i,ProbMinXX), ExpFn(i, ProbMaxXX))
+
+def CDF_table_33():
+ print "\n t P(SECOND_ROTATION <= t)"
+ for i in xrange(1,46):
+ print " %2d %2.5f" % (i, FullCDF(45, i-1))
+
+########### Output ############
+
+# Section 3.1
+rotation_table_31()
+
+# Section 3.3
+expectation_table_33()
+CDF_table_33()
+
+----------------------
+
+1. https://onionbalance.readthedocs.org/en/latest/design.html#overview
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