HIDE & SEEK: Privacy-Preserving Rebalancing on Payment Channel Networks
This addresses the scalability and privacy issues in cryptocurrency payment channels, offering a solution that is incremental by combining existing techniques like linear programming and multi-party computation for improved efficiency and privacy.
The paper tackles the problem of rebalancing funds in payment channel networks, which is necessary to avoid on-chain top-ups when channels are depleted, by introducing a protocol that is both private and globally optimal, maximizing the total rebalanced funds.
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.