Optimizing Virtual Payment Channel Establishment in the Face of On-Path Adversaries

• URL: http://arxiv.org/abs/2011.14341v2
• Date: Thu, 9 May 2024 17:25:34 GMT
• Title: Optimizing Virtual Payment Channel Establishment in the Face of On-Path Adversaries
• Authors: Lukas Aumayr, Esra Ceylan, Yannik Kopyciok, Matteo Maffei, Pedro Moreno-Sanchez, Iosif Salem, Stefan Schmid,
• Abstract summary: We present an integer linear program (ILP) to compute the globally optimal VC setup strategy in terms of transaction costs, security, and privacy. Our results confirm on real-world data that our greedy strategy minimizes costs while protecting against security and privacy threats of on-path adversaries.
• Score: 26.409701991849506
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Payment channel networks (PCNs) are among the most promising solutions to the scalability issues in permissionless blockchains, by allowing parties to pay each other off-chain through a path of payment channels (PCs). However, routing transactions comes at a cost which is proportional to the number of intermediaries, since each charges a fee for the routing service. Furthermore, analogous to other networks, malicious intermediaries in the payment path can lead to security and privacy threats. Virtual channels (VCs), i.e., bridges over PC paths, mitigate the above PCN issues, as an intermediary participates only once to set up the VC and is then excluded from every future VC transaction. However, similar to PCs, creating a VC has a cost that must be paid out of the bridged PCs' balance. Currently, we are missing guidelines to where and how many VCs to set up. Ideally, VCs should minimize transaction costs while mitigating security and privacy threats from on-path adversaries. In this work, we address for the first time the VC setup problem, formalizing it as an optimization problem. We present an integer linear program (ILP) to compute the globally optimal VC setup strategy in terms of transaction costs, security, and privacy. We then accompany the computationally heavy ILP with a fast local greedy algorithm. Our model and algorithms can be used with any on-path adversary, given that its strategy can be expressed as a set of corrupted nodes that is estimated by the honest nodes. We conduct an evaluation of the greedy algorithm over a snapshot of the Lightning Network (LN), the largest Bitcoin-based PCN. Our results confirm on real-world data that our greedy strategy minimizes costs while protecting against security and privacy threats of on-path adversaries. These findings may serve the LN community as guidelines for the deployment of VCs.

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