In this work, we revisit the severely limited throughput problem of cryptocurrencies and propose a novel rebalancing approach for Payment Channel Networks (PCNs). PCNs are a popular solution for increasing the blockchain throughput, however, their benefit depends on the overall users’ liquidity. Rebalancing mechanisms are the state-of-the-art approach to maintaining high liquidity in PCNs. However, existing opt-in rebalancing mechanisms exclude users that may assist in rebalancing for small service fees, leading to suboptimal solutions and under-utilization of the PCNs’ bounded liquidity. We introduce the first rebalancing approach for PCNs that includes all users, following an “all for one and one for all” design philosophy that yields optimal throughput. The proposed approach introduces a double-auction rebalancing problem, which we term Musketeer, where users can participate as buyers (paying fees to rebalance) or sellers (charging fees to route transactions). The desired properties tailored to the unique characteristics of PCNs are formally defined, including the novel property of cyclic budget balance that is a stronger variation of strong budget balance. Basic results derived from auction theory, including an impossibility and multiple mechanisms that either achieve all desiderata under a relaxed model or sacrifice one of the properties, are presented. We also propose a novel mechanism that leverages time delays as an additional cost to users. This mechanism is provably truthful, cyclic budget balanced, individually rational, and economic efficient but only with respect to liquidity.

In this work, we reexamine the vulnerability of Payment Channel Networks (PCNs) to bribing attacks, where an adversary incentivizes blockchain miners to deliberately ignore a specific transaction to undermine the punishment mechanism of PCNs. While previous studies have posited a prohibitive cost for such attacks, we show that this cost can be dramatically reduced (to approximately $125), thereby increasing the likelihood of these attacks. To this end, we introduce Bribe & Fork, a modified bribing attack that leverages the threat of a so-called feather fork which we analyze with a novel formal model for the mining game with forking. We empirically analyze historical data of some real-world blockchain implementations to evaluate the scale of this cost reduction. Our findings shed more light on the potential vulnerability of PCNs and highlight the need for robust solutions.

Payment channel networks (e.g., the Lightning Network in Bitcoin) constitute one of the most popular scalability solutions for blockchains. Their safety relies on parties being online to detect fraud attempts on-chain and being able to timely react by publishing certain transactions on-chain. However, a cheating party may bribe miners in order to censor those transactions, resulting in loss of funds for the cheated party: these attacks are known in the literature as timelock bribing attacks. In this work, we present the first channel construction that does not require parties to be online and, at the same time, is resistant to timelock bribing attacks.

We revisit the severely limited throughput problem of cryptocurrencies and propose a novel rebalancing approach for Payment Channel Networks (PCNs). PCNs are a popular solution for increasing the blockchain throughput, however, their benefit depends on the overall users' liquidity. Rebalancing mechanisms are the state-of-the-art approach to maintaining high liquidity PCNs. However, existing opt-in rebalancing mechanisms exclude users that may assist in rebalancing for small service fees, leading to suboptimal solutions and under-utilization of the PCNs' bounded liquidity.

Off-chain protocols constitute one of the most promising approaches to solve the inherent scalability issue of blockchain technologies. The core idea is to let parties transact on-chain only once to establish a channel between them, leveraging later on the resulting channel paths to perform arbitrarily many peer-to-peer transactions off-chain. While significant progress has been made in terms of proof techniques for off-chain protocols, existing approaches do not capture the game-theoretic incentives at the core of their design, which led to overlooking significant attack vectors like the Wormhole attack in the past. In this work we take a first step towards a principled game-theoretic security analysis of off-chain protocols by introducing the first game-theoretic model that is expressive enough to reason about their security. We advocate the use of Extensive Form Games (EFGs) and introduce two instances of EFGs to capture security properties of the closing and the routing of the Lightning Network. Specifically, we model the closing protocol, which relies on punishment mechanisms to disincentivize parties to upload old channel states on-chain. Moreover, we model the routing protocol, thereby formally characterizing the Wormhole attack, a vulnerability that undermines the fee-based incentive mechanism underlying the Lightning Network.

Payment channel networks (PCNs) are a promising solution to the scalability problem of cryptocurrencies. Any two users connected by a payment channel in the network can theoretically send an unbounded number of instant, costless transactions between them. Users who are not directly connected can also transact with each other in a multi-hop fashion. In this work, we study the incentive structure behind the creation of payment channel networks, particularly from the point of view of a single user that wants to join the network. We define a utility function for a new user in terms of expected revenue, expected fees, and the cost of creating channels, and then provide constant factor approximation algorithms that optimise the utility function given a certain budget. Additionally, we take a step back from a single user to the whole network and examine the parameter spaces under which simple graph topologies form a Nash equilibrium.

Payment channel networks (PCNs) are one of the most prominent solutions to the limited transaction throughput of blockchains. Nevertheless, PCNs suffer themselves from a throughput limitation due to the capital constraints of their channels. A similar dependence on high capital is also found in inter-bank payment settlements, where the so-called netting technique is used to mitigate liquidity demands.

The construction of r-nets offers a powerful tool in computational and metric geometry. We focus on high- dimensional spaces and present a new randomized algorithm which efficiently computes approximate r-nets with respect to Euclidean distance. For any fixed ∊ > 0, the approximation factor is 1 + ∊ and the complexity is polynomial in the dimension and subquadratic in the number of points. The algorithm succeeds with high probability. Specifically, we improve upon the best previously known (LSH- based) construction of Eppstein et al. [EHS15] in terms of complexity, by reducing the dependence on ∊, provided that ∊ is sufficiently small. Our method does not require LSH but, instead, follows Valiant's [Val15] approach in designing a sequence of reductions of our problem to other problems in different spaces, under Euclidean distance or inner product, for which r-nets are computed efficiently and the error can be controlled. Our result immediately implies efficient solutions to a number of geometric problems in high dimension, such as finding the (1 + ∊)-approximate kth nearest neighbor distance in time subquadratic in the size of the input.