HIDE & SEEK: Privacy-Preserving Rebalancing on Payment Channel Networks

TL;DR

This paper introduces a privacy-preserving, globally optimal rebalancing protocol for payment channel networks that uses multi-party computation and linear programming to maximize rebalanced funds while maintaining user privacy.

Contribution

It presents a novel opt-in rebalancing protocol that ensures privacy, optimality, and incentive compatibility using multi-party computation and linear programming techniques.

Findings

Protocol maximizes total rebalanced funds.

Ensures privacy through multi-party computation.

Decomposes solutions into incentive-compatible cycles.

Abstract

Payment channels effectively move the transaction load off-chain thereby successfully addressing the inherent scalability problem most cryptocurrencies face. A major drawback of payment channels is the need to ``top up'' funds on-chain when a channel is depleted. Rebalancing was proposed to alleviate this issue, where parties with depleting channels move their funds along a cycle to replenish their channels off-chain. Protocols for rebalancing so far either introduce local solutions or compromise privacy. In this work, we present an opt-in rebalancing protocol that is both private and globally optimal, meaning our protocol maximizes the total amount of rebalanced funds. We study rebalancing from the framework of linear programming. To obtain full privacy guarantees, we leverage multi-party computation in solving the linear program, which is executed by selected participants to maintain efficiency. Finally, we efficiently decompose the rebalancing solution into incentive-compatible cycles which conserve user balances when executed atomically. Keywords: Payment Channel Networks, Privacy and Rebalancing.