Balance disclosure in payment channel networks using noiseless privacy

Corcoran, Padraig ORCID: https://orcid.org/0000-0001-9731-3385 and Spasic, Irena ORCID: https://orcid.org/0000-0002-8132-3885 2025. Balance disclosure in payment channel networks using noiseless privacy. Distributed Ledger Technologies: Research and Practice

Preview

PDF - Accepted Post-Print Version Download (965kB) | Preview

Abstract

Payment Channel Networks (PCNs) represent an emerging approach to improving the scalability of cryptocurrencies. A PCN provides the ability to consolidate a larger set of payments into a smaller set where individual payments are immediately confirmed. It consists of a network of payment channels between pairs of peers, where each channel has a total capacity that is split into two directional balances, determining the maximum payment amount that can be forwarded in either direction. If two peers do not have a direct channel between them, they can still make a corresponding payment using a path in the network where all channels in this path have a sufficient balance to forward the payment in question. Whenever a channel is used to make a payment, its balances are updated accordingly. To provide payment privacy, it is standard practice for channels not to disclose their current balances. However, this complicates the task of path planning, as a trial-and-error approach is often required in the search for a feasible path. In this article, we propose a novel method for disclosing channel balance information in a manner that both provides payment privacy and supports the task of path planning. This is achieved by applying methods from the field of Noiseless Privacy (NP) that allow balance summary statistics to be disclosed while obscuring the details of individual payments. Using a simulation of payments on the Lightning Network, which is built on top of the Bitcoin cryptocurrency, we demonstrate the trade-off between the level of privacy provided and the resulting effectiveness of path planning.

Item Type:	Article
Status:	In Press
Schools:	Schools > Computer Science & Informatics
Publisher:	Association for Computing Machinery (ACM)
Date of First Compliant Deposit:	30 September 2025
Date of Acceptance:	22 September 2025
Last Modified:	30 Sep 2025 13:45
URI:	https://orca.cardiff.ac.uk/id/eprint/181366

Actions (repository staff only)

Edit Item