Payment Channel Networks Research Articles

Path Planning in Payment Channel Networks with Multi-Party Channels

Payment Channel Networks (PCNs) provide a means to improve the scaling of cryptocurrency payments by allowing peers to make payments between themselves in an efficient manner. To make a payment between two peers, the task of path planning must first be performed to determine a path in the PCN connecting the peers in question before the payment in question is performed using this path. To date, existing research has focused on the problem of performing path planning in PCNs that contain two-party channels. It has been hypothesised that the scaling of PCNs could be further improved by considering the inclusion of multi-party channels that contain more than two peers. However, the problem of performing path planning in PCNs that contain multi-party channels has not yet been considered. In this article, we address this gap in the research literature and propose a novel path planning method for PCNs containing multi-party channels. This method involves modelling the PCN with multi-party channels as a hypergraph, a type of graph where edges can contain two or more vertices, and using this model to solve the path planning problem in question. We prove that the proposed method is correct and computationally efficient. Furthermore, assuming path planning is performed using this method, we also present theoretical and experimental analyses that demonstrate the scaling benefits of using multi-party channels.

Deadlock Prevention in Payment Channel Networks

Deadlock Prevention in Payment Channel Networks

Toward Aggregated Payment Channel Networks

Toward Aggregated Payment Channel Networks

Flexichain: Flexible Payment Channel Network to Defend Against Channel Exhaustion Attack

The payment channel network (PCN) is an effective off-chain scaling solution widely recognized for reducing operational costs on permissionless blockchains. However, it still faces challenges such as lack of flexibility, channel exhaustion, and poor sustainability. Currently, a separate deposit is required for each payment channel, which locks a significant amount of coins for a longer period. This restricts the ability to move locked coins across their channels off-chain. Additionally, unbalanced (unidirectional) transfers can lead to channel exhaustion, rendering the channel unsustainable. To address these issues, we propose a novel off-chain flexible PCN protocol called Flexichain . Unlike existing approaches, Flexichain allows users to deposit coins per user rather than per channel. This provides flexibility to move coins freely between channels without relying on the blockchain or disrupting the off-chain cycle. Flexichain is proven to be secure under the Universal Composability framework and resistant against channel exhaustion attacks. To assess the performance of Flexichain, we conduct experiments on both on-chain and off-chain operations using snapshots of the Lightning Network. We evaluate the on-chain gas costs, success ratio and success amount of off-chain payments under uniform and skewed payment demands, as well as the computational and communication overheads of the off-chain contracts.

ProfitPilot: Enabling Rebalancing in Payment Channel Networks Through Profitable Cycle Creation

ProfitPilot: Enabling Rebalancing in Payment Channel Networks Through Profitable Cycle Creation

Card Payment Protocol for Cryptocurrencies with Payment Channel Network

Cryptocurrency, despite the upsurge as a speculative investment, is still a long way from being people’s money. The extreme technicality poses a significant barrier for the general public to adopt it as a medium of exchange. Therefore, simplifying the payment process is a predominant necessity; for example, facilitation in adopting conventional payment instruments can bring convenience to both merchants and consumers. This paper proposes a novel cryptocurrency card payment protocol leveraging the Payment Channel Network (PCN) concept, facilitating high throughput and affordable transactions. Our design is based on the Bitcoin-backed Lightning Network (LN) with adjustments to make it executable by a smart card. It also preserves the decentralized nature of cryptocurrencies, reduces operational costs in several ways, and allows instant settlement for the recipients as compared to both conventional and cryptocurrency card payment systems. Our game theoretical analysis, where we model the engagement between the cardholder and the card agent as a long-run extensive form game, attests that they accord with the protocol without experiencing any honest loss under pragmatic conditions. We also discuss the privacy features compared to conventional card payments and LN. The protocol can function independently, without the need for trust, and can also be regulated for those seeking government mediation. This approach can potentially revolutionize the payment landscape by allowing the public to use cryptocurrency for everyday transactions conveniently and allowing existing cryptocurrency holders to conduct affordable micropayments.

Assessing Routing Algorithms for Payment Channel Networks

Payment Channel Networks (PCNs) are a promising approach to overcome scalability issues of blockchains. To achieve efficient payments in PCNs, it is necessary to route transactions between a payer and a payee. Especially in large-scale PCNs, multi-hop routing becomes necessary, since transactions need to be relayed by nodes. For this, a scalable routing algorithm is needed, which fits the individual objectives of PCN users. In this article, we study whether routing protocols from the field of Wireless Sensor Networks can be applied in PCNs. To this end, we first derive requirements for routing in PCNs, select suitable approaches, and analyze to which degrees they perform well and meet the requirements. We adapt selected protocols and evaluate them with regard to the lengths of payment paths, fees, and success ratio.

Multi‐hop anonymous payment channel network based on onion routing

AbstractPayment channel networks (PCNs) are one of the most promising off‐chain solutions to the blockchain scalability problem. However, most current payment channel schemes suffer from overly ideal routing assumptions or fail to provide complete path privacy protection. To address the above problems, the multi‐hop anonymous and privacy‐preserving PCN scheme are analyzed and a multi‐hop anonymous PCN based on onion routing is proposed based on it. The scheme removes the routing assumptions of the original scheme, designs an onion routing‐based payment channel network, and modifies the way relay nodes construct elliptic curve discrete logarithmic problems to resist wormhole attacks. Finally, the security analysis shows that the scheme in this paper has atomicity, relational anonymity and resistance to wormhole attacks. The latest bilinear pairing anonymous multi‐hop payment (EAMHL+) scheme is used to compare communication and computing overhead. The results show that the scheme in this paper requires less communication overhead and is more efficient in terms of computational overhead when the average hop number of the payment channel network is 3 hops and the network diameter is 6 hops.

Bitcoin Layer Two Scaling Solutions: Lightening Payment Channels Network Comprehensive Review, Mechanisms, Challenges, Open Issues and Future Research Directions

Blockchain technology era is triggered due to current advancement in the decentralized paradigms via facilitating secure collaboration among untrusted entities consequently superseding the necessity for the trusted third parties’ existence. Notwithstanding its potential, blockchain scalability faces severe limitations such as low throughput, high fees, and confirmation time latency. Several scaling solutions have been proposed, including layer one solutions that substantially require fundamentally amending blockchain underlying infrastructure resulted in further scalability complications. Layer two solutions, specifically Lightning Network, is a successful cryptographic layer built atop of the Bitcoin blockchain that aims to alleviate these implications by deporting transaction processing outside blockchain while ensuring security and consensus inherited from blockchain without compromising its infrastructure. Payment channels form the core of the lightning network, facilitating fast and secure transactions between participants. However, the network encounters substantial obstacles associated with transaction amounts that exceed the current channel capacity, leading to payment failures and limiting its effectiveness. Payment Channel Networks (PCN) have been risen as an evolved replacement that solves payment channel’s issues, seeking to enhance transaction throughput and reduce confirmation delays by solving limited capacities shortages. This paper thoroughly examines PCN efficiency impacting factors that hinder its current adoption growth and avoiding its widespread employment. Additionally, existing solutions are reviewed and categorized based on the PCN building architecture. The research concludes by outlining potential research avenues and future directions to improve the efficiency and practicality of the Lightning Network PCN. This research contribution aids the advent of blockchain layer two technology and participates in its integration into real-world applications.

Fence: Fee-Based Online Balance-Aware Routing in Payment Channel Networks

Fence: Fee-Based Online Balance-Aware Routing in Payment Channel Networks

SAMCU: Secure and Anonymous Multi-Channel Updates in Payment-Channel Networks

SAMCU: Secure and Anonymous Multi-Channel Updates in Payment-Channel Networks

An Integrated Stablecoin and Subsidised Node Design for Payment Channel Networks

An Integrated Stablecoin and Subsidised Node Design for Payment Channel Networks

Topologies for Blockchain Payment Channel Networks: Models and Constructions

Topologies for Blockchain Payment Channel Networks: Models and Constructions

Anonymous Multi-Hop Payment for Payment Channel Networks

Anonymous Multi-Hop Payment for Payment Channel Networks

Deep Reinforcement Learning Based Scheduling Strategy in Blockchain Payment Channel Networks

Deep Reinforcement Learning Based Scheduling Strategy in Blockchain Payment Channel Networks

Survivable Payment Channel Networks

Survivable Payment Channel Networks

Design and evaluation of Swift routing for payment channel network

Payment Channel Networks (PCNs) are a promising alternative to improve the scalability of a blockchain network. A PCN employs off-chain micropayment channels that do not need a global block confirmation procedure, thereby sacrificing the ability to confirm transactions instantaneously. PCN uses a routing algorithm to identify a path between two users who do not have a direct channel between them to settle a transaction. The performance of most of the existing centralized path-finding algorithms does not scale with network size. The rapid growth of Bitcoin PCN necessitates considering distributed algorithms. However, the existing decentralized algorithms suffer from resource underutilization. We present a decentralized routing algorithm, Swift, focusing on fee optimization. The concept of a secret path is used to reduce the path length between a sender and a receiver to optimize the fees. Furthermore, we reduce a network structure into combinations of cycles to theoretically study fee optimization with changes in cloud size. The secret path also helps in edge load sharing, which results in an improvement of throughput. Swift routing achieves up to 21% and 63% in fee and throughput optimization, respectively. The results from the simulations follow the trends identified in the theoretical analysis.

A centrality analysis of the Lightning Network

Blockchain technology has a huge impact on our digital society by enabling a more decentralized economy and policy making. This decentralization is also pivotal in payment Payment channel networks (PCNs), including the Lightning Network, have emerged as a promising solution to the scalability challenges that many blockchain-based cryptocurrencies, like Bitcoin, grapple with. These PCNs, while innovative, also inherit the rigorous dependability demands of the blockchain. A pivotal aspect of this dependability is the need for a high degree of decentralization, essential for mitigating liquidity bottlenecks and on-path attacks.Driven by this imperative, our research embarks on an empirical centrality analysis of the Lightning Network, with a keen focus on the betweenness centrality distribution of its routing system. Utilizing an extensive dataset, sourced from several millions of broadcasted messages via the gossip protocol, we introduce the TimeMachine tool, an innovative method that allows for a temporal exploration of the network’s evolution.Our findings reveal that while the Lightning Network exhibits a commendable level of decentralization, there is a discernible skew: a limited set of nodes command a significant portion of the transactions. Alarmingly, over the past two years, the network’s centrality has surged, with the inequality, as gauged by the Gini index, rising by over 15 uptick of approximately 5 in. This research not only uncovers critical insights into the Lightning Network’s structural dynamics but also raises the question about strategies and policies that ensure its sustained decentralization in the face of evolving challenges such as security vulnerabilities, potential monopolistic tendencies, liquidity bottlenecks, the risk of transaction censorship and many more.

Utility-Aware Payment Channel Network Rebalance

The payment channel network (PCN) is a promising solution to increase the throughput of blockchains. However, unidirectional transactions can deplete a user's deposits in a payment channel (PC), reducing the success ratio of transactions (SRoT). To address this depletion issue, rebalance protocols are used to shift tokens from well-deposited PCs to under-deposited PCs. To improve SRoT, it is beneficial to increase the balance of a PC with a lower balance and a higher weight (i.e., more transaction executions rely on the PC). In this paper, we define the utility of a transaction and the utility-aware rebalance (UAR) problem. The utility of a transaction is proportional to the weight of the PC and the amount of the transaction, and inversely proportional to the balance of the receiver. To maximize the effect of improving SRoT, UAR aims to find a set of transactions with maximized utilities, satisfying the budget and conservation constraints. The budget constraint limits the number of tokens shifted in a PC. The conservation constraint requires that the number of tokens each user sends equals the number of tokens received. We prove that UAR is NP-hard and cannot be approximately solved with a constant ratio. Thus, we propose two heuristic algorithms, namely Circuit Greedy and UAR_DC. Extensive experiments show that our approaches outperform the existing approach by at least 3.16 times in terms of utilities.

A Distributed and Privacy-Aware High-Throughput Transaction Scheduling Approach for Scaling Blockchain

Payment channel networks (PCNs) are considered as a prominent solution for scaling blockchain, where users can establish payment channels and complete transactions in an off-chain manner. However, it is non-trivial to schedule transactions in PCNs and most existing routing algorithms suffer from the following challenges: 1) one-shot optimization, 2) privacy-invasive channel probing, 3) vulnerability to DoS attacks. To address these challenges, we propose a privacy-aware transaction scheduling algorithm with defence against DoS attacks based on deep reinforcement learning (DRL), namely PTRD. Specifically, considering both the privacy preservation and long-term throughput into the optimization criteria, we formulate the transaction-scheduling problem as a Constrained Markov Decision Process. We then design PTRD, which extends off-the-shelf DRL algorithms to constrained optimization with an additional cost critic-network and an adaptive Lagrangian multiplier. Moreover, considering the distribution nature of PCNs, in which each user schedules transactions independently, we develop a distributed training framework to collect the knowledge learned by each agent so as to enhance learning effectiveness. With the customized network design and the distributed training framework, PTRD achieves a good balance between the optimization of the throughput and the minimization of privacy risks. Evaluations show that PTRD outperforms the state-of-the-art PCN routing algorithms by 2.7%–62.5% in terms of the long-term throughput while satisfying privacy constraints.