  Payment channel networks are a promising approach to improve the scalability
of cryptocurrencies: they allow to perform transactions in a peer-to-peer
fashion, along multi-hop routes in the network, without requiring consensus on
the blockchain. However, during the discovery of cost-efficient routes for the
transaction, critical information may be revealed about the transacting
entities.
  This paper initiates the study of privacy-preserving route discovery
mechanisms for payment channel networks. In particular, we present LightPIR, an
approach which allows a source to efficiently discover a shortest path to its
destination without revealing any information about the endpoints of the
transaction. The two main observations which allow for an efficient solution in
LightPIR are that: (1) surprisingly, hub labelling algorithms - which were
developed to preprocess "street network like" graphs so one can later
efficiently compute shortest paths - also work well for the graphs underlying
payment channel networks, and that (2) hub labelling algorithms can be directly
combined with private information retrieval.
  LightPIR relies on a simple hub labeling heuristic on top of existing hub
labeling algorithms which leverages the specific topological features of
cryptocurrency networks to further minimize storage and bandwidth overheads. In
a case study considering the Lightning network, we show that our approach is an
order of magnitude more efficient compared to a privacy-preserving baseline
based on using private information retrieval on a database that stores all
pairs shortest paths.
