An Empirical Analysis of Privacy in the Lightning Network
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Payment channel networks, and the Lightning Network in particular, seem to offer a solution to the lack of scalability and privacy offered by Bitcoin and other blockchain-based cryptocurrencies. Previous research has already focused on the scalability, availability, and crypto-economics of the Lightning Network, but relatively little attention has been paid to exploring the level of privacy it achieves in practice. This paper presents a thorough analysis of the privacy offered by the Lightning Network. We present three main attacks that exploit publicly available information about the network topology and its active nodes and channels in order to learn information that is designed to be kept secret, such as how many coins a node has available to spend or who the sender and recipient are in a payment routed through the network. We evaluate one of our attacks on the live network and, due to cost and ethical considerations, evaluate our other two attacks on a simulated Lightning network that faithfully mimics the real one.
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