LASTor : A Low-Latency AS-Aware Tor Client

LASTor: A Low-Latency AS-Aware Tor Client MasoudAkhoondi, Curtis Yu, HarshaV. Madhyastha

Tor (The onion router) D R3 R2 S R1 Anonymity - Each hop only knows previous and next hop on a path Low latency communication - 90% of Tor traffic is interactive [Mccoy08] • 400,000 users • 2700 relays

How are latencies on Tor? • Experiment: • Sources: • 50 PlanetLabnodes spread across globe • Destinations: • Top 200 websites 5x inflation in median

Profiling attack on Tor Green AS (Autonomous System) can eavesdrop on both end segments of path[Murdoch07] D Exit segment S Entry segment Relay 2 Entry relay Exit relay

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

LASTor: A low-latency AS-aware Tor client Main insight: Client modifications suffice Mitigate profiling attack Improve poor latency for interactive communications

Main insight: Client modifications suffice Mitigate profiling attack Improve poor latency for interactive communications • Solution: • Modified path selection to reduce latency • Solution: • AS-aware path selection

Goal: Improve latency Sources of latency on Tor • Queuing and processing delay • Congestion in relays [Panchenko09] • Propagation delay • Long paths D S

Goal: Improve latency Shortest path vs. Default Tor • Destinations: • Top 200 websites • Sources: • 50 PlanetLab nodes spread across globe • Map relays to geographical locations 50% improvement in median Shorter paths can greatly reduce latency Path should not be deterministic  Weighted Shortest Path (WSP)

Goal: Improve latency Weighted Shortest Path (WSP) • WSP computes length of all possible paths • Probability of choosing is inversely proportional to its length 3 3 1 1 1 2 4 3

Goal: Improve latency An Attack on WSP Attacker controls a relay 3 3 1 1 1 2 4 3

Goal: Improve latency Solution: Clustering of relays 3 3 1 1 1 2 4 3 • Run WSP using clusters of relays • For chosen cluster-level path, randomly pick arelay in each cluster

Goal: Improve latency Solution: Clustering of relays 3 3 1 1 1 2 4 3

Goal: Improve latency 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

Goal: Improve latency WSP reduces latency 50 PlanetLab nodes to top 200 websites 20% improvement in 80th percentile 25% improvement in median

Goal: Improve latency 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 1 0 α Lowest latency Highest anonymity

Goal: Improve latency Tunable path selection in LASTor Lower α, lower latency Higher α, higher anonymity • Gini Coefficient • measure of inequality in a distribution • 0: perfect equality • 1: maximal inequality

Main insight: Client modifications suffice Mitigate profiling attack Improve poor latency for interactive communications • Solution: • Modified path selection to reduce latency • Solution: • AS-aware path selection

Goal: AS-aware Profiling attack on a path Goal: Detect common ASeson entry and exit segments Green AS (Autonomous System) can eavesdrop on both end segments of path[Murdoch07] Exit segment Entry segment D Relay 2 S Entry relay Exit relay

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 Exit relay D

Goal: AS-aware Our solution: AS set prediction Exit relay D Predict ASes on all paths compliant with routing policies

Goal: AS-aware 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 AS1AS2AS3 • Algorithm • Modified version of Dijkstra’s algorithm • Output • Set of ASes on policy-compliant routes

Goal: AS-aware AS set based prediction is accurate 11% of common AS instances are missed 57% of common AS instances are missed • False negative: fraction of paths with common AS not detected Any path selection algorithm can use AS set predcitionto avoid profiling attack

LASTor Latency 50 PlanetLab nodes to top 200 websites

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%

Thank you

How does Tor work? (Onion Routing) Entry Relay (guard) Server Exit Relay Client R1 R3 R5 R4 R2 - 300,000 users - 2700 relays Middle Relay

Is distance a good estimation of latency? • Choose two different paths: • WSP(latency) • WSP(distance) • Measure latency on these two paths • 50 planetlab nodes as source and top 200 websites as destination There is no significant difference between these two metrics

Goal: AS-aware Accuracy of AS-set prediction algorithm

Attack on WSP Goal: Improve latency • Clustering of relays reduces: • Probability of the attack • Running time of WSP 50% reduction • Adversary replicates 10% most popular relays 25 times • Compute probability of the chosen path traversing a malicious relay

LASTor : A Low-Latency AS-Aware Tor Client