面对路径上敌手时优化虚拟支付通道的建立
Optimizing Virtual Payment Channel Establishment in the Face of On-Path Adversaries
AI总结：
本文首次将虚拟支付通道建立形式化为优化问题，提出兼顾交易成本、安全与隐私的 ILP 及快速贪心算法，并在 Lightning Network 快照上验证其有效性。
摘要：
支付通道网络（PCN）是解决无许可区块链可扩展性问题最有前景的方案之一，它允许各方通过一条由支付通道（PC）组成的路径进行链下支付。然而，路由交易会产生与中介数量成比例的成本，因为每个中介都会为路由服务收取费用。此外，与其他网络类似，支付路径中的恶意中介可能带来安全和隐私威胁。虚拟通道（VC），即跨越 PC 路径的桥接，可缓解上述 PCN 问题，因为中介仅在建立 VC 时参与一次，随后便被排除在未来的每一笔 VC 交易之外。然而，与 PC 类似，创建 VC 也有成本，并且必须从被桥接的 PC 的余额中支付。目前，我们缺少关于应在何处建立多少 VC 的指导原则。理想情况下，VC 应在最小化交易成本的同时，缓解来自路径上敌手的安全和隐私威胁。在本工作中，我们首次处理 VC 建立问题，并将其形式化为一个优化问题。我们提出一个整数线性规划（ILP），用于在交易成本、安全性和隐私方面计算全局最优的 VC 建立策略。随后，我们为计算开销较大的 ILP 配套了一种快速的局部贪心算法。只要路径上敌手的策略可以表示为一组由诚实节点估计出的被腐化节点，我们的模型和算法就可用于任意此类敌手。我们在 Lightning Network（LN，最大的基于 Bitcoin 的 PCN）的一个快照上对该贪心算法进行了评估。我们的结果在真实世界数据上证实，我们的贪心策略能够最小化成本，同时防范路径上敌手造成的安全和隐私威胁。这些发现可为 LN 社区部署 VC 提供指导原则。
英文摘要：
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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