Lightning Network Anonymity

An attack that enables an intermediary to break the anonymity of the source and destination of a trannsaction in the Lightning network.

This includes a simulator to simulate transactions using LND(https://github.com/lightningnetwork/lnd/blob/master/routing/pathfind.go), c-Lightning(https://github.com/ElementsProject/lightning/blob/f3159ec4acd1013427c292038b88071b868ab1ff/common/route.c) and Eclair(https://github.com/ACINQ/eclair/blob/master/eclair-core/src/main/scala/fr/acinq/eclair/router/Router.scala).

All path finding algorithms are based on the Dijkstra's algorithm using a priority queue(https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm).

We modify Eclair to use a generalized version of Dijkstra's algorithm(https://en.wikipedia.org/wiki/K_shortest_path_routing) instead of using Yen's algorithm(https://en.wikipedia.org/wiki/Yen%27s_algorithm). We do not have code to simulate concurrent payments yet.

The experiment is run on a snapshot of the Lightning Network obtained from https://ln.fiatjaf.com/. The set of adversaries is a mixture of nodes with high centrality, low centrality and random nodes. The snapshot as well as the centralities of all nodes are found in data/Snapshot and data/Centrality respectively.

Code Structure

populate_graph.py - Creates a payment graph from a snapshot of the Lightning Network.

pathFind.py - Implements Dijkstra and generalized Dijkstra(for 3 best paths) taking the cost function of either LND, c-Lightning or Eclair as argument.

attack_mixed.py - Implements an attack where an intermediary finds all potential sources and destinations of a transaction that it is a part of.

centrality.py - Uses the data from data/Snapshot/ to calculate different centrality metrics to help select adversaries within the network and generate csv report files.

modification.py - Implements adding random hops to a short path and checking whether a suboptimal contains random hops with respect to an optimal one.

execute.py - Runs a sample experiment with a set of adversaries on transactions between random pairs of sources and destinations.

demo.py - Runs a sample experiment using demo graph structures (barabasi_albert or erdos_renyi construction) using nodes with highest centrality as adversaries.

utils.py - Utility functions that are used within the simulation framework.

results.py - Generates statistics and plots from the anonymity sets.

Running an experiment

• Choose the set of adversaries using the centrality data from data/Centrality as your guide.
• Specify the number of transactions you want to simulate in a run.
• Specify the mechanism for choosing source destination pairs for each transaction.
• The details of the transactions and the results of each attack instance is copied to results.json

Example commands

echo '[]' > results/results.json
python3 demo.py random demo500 1000
python3 results.py demo500

Data collection

• Download database from lnchannels
• Import the file lnchannels.dump into an SQL database
• Run the following queries (PostgreSQL) to generate the snapshot files

nodes.csv

\COPY (SELECT row_number() over (order by pubkey) as id, pubkey, alias, color, software, oldestchannel, openchannels, closedchannels, capacity, avg_duration, avg_open_fee, avg_close_fee, close_types From nodes) TO '/tmp/nodes.csv' WITH CSV DELIMITER ',' HEADER

channels.csv

\COPY (SELECT row_number() over (order by short_channel_id) as id, short_channel_id, satoshis, nodes, txs, close, open, closer, last_update, a, b, funder From channels) TO '/tmp/channels.csv' WITH CSV DELIMITER ',' HEADER

policies.csv

\COPY (SELECT row_number() over (order by short_channel_id) as id, short_channel_id, direction, base_fee_millisatoshi, fee_per_millionth, delay, update_time From policies) TO '/tmp/policies.csv' WITH CSV DELIMITER ',' HEADER

Results

The following results can be obtained from results.py:

• A figure plotting the sizes of each source and destination anonymity set.
• Number of transactions for which either source or the destination anonymity set was a singular set or both.
• The number of transactions attacked by each adversary.
• Correlation of the number of hops from the adversary to source/destination to the size of source/destination anonymity set.
• The number of false positives found for sources, destinations, or both.
• The average hop count and fee of the transactions.