LASTor : A Low-Latency AS-Aware Tor Client

LASTor: A Low-Latency AS-Aware Tor Client

Masoud Akhoondi, Curtis Yu, Harsha V. Madhyastha

Tor (The onion router)

S

D

R1

R2

R3

• 400,000 users• 2700 relays

• Anonymity- Each hop only knows previous and next hop on a path

• Low latency communication- 90% of Tor traffic is interactive [Mccoy08]

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How are latencies on Tor?

• Experiment:– Sources:

• 50 PlanetLab nodes spread across globe

– Destinations:• Top 200 websites 5x inflation

in median

Exit relay

Relay 2

Entry relay

Profiling attack on Tor

Entrysegment

Exitsegment

Green AS (Autonomous System) can eavesdrop on both end segments of path[Murdoch07]

DS

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How severe is profiling attack?

65% of relays are in 20% of all ASes

Non-uniform distribution of relays across ASes

Potential solution for these problems

• Measure latencies and routes from each relay to all end-hosts [Sherr09, Alsabah11, Mittall11]– Requires modification of relays

• None of these proposals deployed yet– Non-trivial to implement

Main insight: Client modifications suffice

Improve poor latency for interactive communications

Mitigate profiling attack

LASTor: A low-latency AS-aware Tor client


Solution: Modified path

selection to reduce latency

Solution: AS-aware path selection



Sources of latency on Tor• Queuing and processing delay

– Congestion in relays [Panchenko09]• Propagation delay

– Long paths

Goal: Improve latency

D

S

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Shortest path vs. Default Tor

• Destinations:– Top 200 websites

• Sources:– 50 PlanetLab nodes

spread across globe• Map relays to

geographical locations

Shorter paths can greatly reduce latency

50% improvement in median


Path should not be deterministic Weighted Shortest Path (WSP)

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Weighted Shortest Path (WSP)• WSP computes length of all possible paths• Probability of choosing is inversely proportional

to its length


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1 3

2

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1

3

Path Length Prob.Upper

8 0.56

Lower 10 0.44


An Attack on WSP

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13

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3

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1

3

Original prob. Prob.Compromised paths 0.56 0.8

Other paths 0.44 0.2

Attacker controls a relay


Solution: Clustering of relays

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13

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• Run WSP using clusters of relays• For chosen cluster-level path, randomly pick a relay in each cluster


Solution: Clustering of relays

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13

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3

Prob.Compromised paths 0.56

Other paths 0.44

Weighted Shortest Path (WSP)

• Preprocessing– Cluster all relays

• Path selection– Computes length of possible paths using clusters– Choose a path with a probability inversely proportional to

its length– Pick a relay randomly in each chosen cluster

• Other issues (see paper)– Handling multi-location destinations– Choosing entry relays


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WSP reduces latency

50 PlanetLab nodes to top 200 websites

20% improvement in 80th percentile


25% improvement in median

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Tunable path selection in LASTor

• Modify WSP to consider user’s preference towards:– Anonymity – Latency

• Single parameter α configured by user:– Modified weight w to w(1-α) where 0 ≤α≤ 1

0 1α

Lowest latency Highest anonymity


Tunable path selection in LASTor

Gini Coefficient measure of inequality in a distribution 0: perfect equality 1: maximal inequality

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Lower α, lower latency Higher α, higher anonymity



Solution:Modified path

selection to reduce latency

Solution: AS-aware path selection



Exit relay

Relay 2

Entry relay

Profiling attack on a path

Entrysegment

Exitsegment

Green AS (Autonomous System) can eavesdrop on both end segments of path[Murdoch07]

Goal: Detect common ASes on entry and exit segments

DS

Goal: AS-aware

Simple heuristic does not work

• Default Tor ensures no two Tor relays in same /16• False negative: fraction of paths with common AS not detected

57% of common AS instances are missed

Goal: AS-aware

Need for predicting AS paths• Approach 1: Measure routes from relays to all end hosts

– Need to modify relays• Approach 2: Infer AS-level routes

– Several techniques exist [Mao05, Madhyastha06, Madhyastha09, Lee11]

– At best 70% accuracy

Goal: AS-aware

Exit relay D

Our solution: AS set predictionGoal: AS-aware

Predict ASes on all paths compliant with routing policies

Exit relay D

Our solution: AS set prediction

• Input [13MB initially, 1.5MB weekly]– Topology graph at AS-level– Estimate of AS path length– Compact representation routing policies:• Triple of (AS1, AS2, AS3) where AS1AS2AS3

• Algorithm– Modified version of Dijkstra’s algorithm

• Output– Set of ASes on policy-compliant routes

Goal: AS-aware

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AS set based prediction is accurate


Goal: AS-aware


• False negative: fraction of paths with common AS not detectedAny path selection algorithm can use AS set predcition to avoid profiling attack

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LASTor Latency

50 PlanetLab nodes to top 200 websites

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Summary

• Demonstrated client side changes are sufficient for:– Lower latency– Higher anonymity

• Designed and implemented LASTor– Reduces median latency by 25%– Reduces median false negative of common AS from 57% to 11%

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Thank you

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How does Tor work? (Onion Routing)

R4 R5

R3

R2

R1

ServerClient

Entry Relay (guard)

Middle Relay

Exit Relay

- 300,000 users- 2700 relays

Is distance a good estimation of latency?

• Choose two different paths:– WSP(latency)– WSP(distance)

• Measure latency on these two paths

There is no significant difference between these two metrics 31

• 50 planetlab nodes as source and top 200 websites as destination

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Accuracy of AS-set prediction algorithmGoal: AS-aware

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Clustering of relays reduces:- Probability of the attack- Running time of WSP

• Adversary replicates 10% most popular relays 25 times• Compute probability of the chosen path traversing a malicious relay

Goal: Improve latencyAttack on WSP

50% reduction

LASTor : A Low-Latency AS-Aware Tor Client

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