LightPIR: Privacy-Preserving Route Discovery for Payment Channel Networks

TL;DR

LightPIR introduces a privacy-preserving route discovery method for payment channel networks, enabling efficient shortest path finding without revealing transaction endpoints, leveraging hub labeling algorithms combined with private information retrieval.

Contribution

The paper presents LightPIR, a novel approach that combines hub labeling algorithms with PIR to enhance privacy and efficiency in route discovery for payment networks.

Findings

LightPIR is an order of magnitude more efficient than baseline methods.

Hub labeling algorithms are effective for payment network graphs.

The approach minimizes storage and bandwidth overheads.

Abstract

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.