Payment Channel Networks Research Articles

A Blockchain-Based Method for Optimizing the Routing of High-Frequency Carbon-Trading Payment Channels

Carbon trading is an effective way to achieve carbon neutrality. It is a market mechanism aimed at reducing global greenhouse gas emissions and carbon dioxide emissions. Blockchain technology can be applied to the carbon-trading scenario using characteristics that guarantee the security, decentralization, data immutability, and data traceability of the carbon-trading process. It would be difficult to implement carbon trading on blockchains for all enterprises and individuals, as the current performance of blockchains does not meet the requirements. There has been extensive research conducted on blockchain performance optimization, and the off-chain payment channel is one of the more mature solutions. This approach involves the transfer of transactions to off-chain transactions, thus avoiding high transaction fees. Existing research has addressed the problem of routing security and efficiency, with less emphasis on factors such as routing transaction costs, node reputation, and routing path considerations. This paper researches the optimization of payment routing in Payment Channel Networks (PCNs) and proposes the Multi-Factor Routing Payment Scheme (MFPS), which integrates factors such as the node reputation, transaction fee cost, and distance to select the route for payment transactions. In order to improve the success ratio of routing transactions, the transaction-splitting algorithm is proposed. To ensure the security and privacy of the transaction process, the Asymmetric Time-Lock Contract (ATLC) protocol is proposed. The results of extensive experimental simulations show that the MFPS proposed in this paper outperforms the ShortestPath, Cheapest, and SplitDistance algorithms. It achieves an approximately 13.8%∼49% improvement in the transaction success ratio and reduces the average transaction processing cost. The security and privacy measures can defend against wormhole and double-flower attacks and exhibit better performance in terms of computational verification and message overhead.

Coalition Formation Game for Cost-Efficient Multiparty Payment Channel in Payment Channel Networks.

Blockchain has introduced a new era for online payment services and its economy with tamper-proof cryptocurrencies. However, blockchain, which is based on global peer-to-peer networks, has its limitations due to payment delays from global consensus and transaction costs for maintenance. Thus, payment channel networks (PCN) have been proposed as one of the most promising off-chain solutions, allowing users to pay directly through payment channels (PC), with minimal blockchain involvement. However, payment delays and cost problems still exist, especially given the large size of the PCN. This study proposes a multiparty payment channel (MPC) that enables multiple users to join the same PC and exchange payment transactions, compared to the legacy PC. To avoid a consensus procedure among users in the PC, we introduce sequential and parallel updates for the PC status. Since increasing the MPC size limits the advantages in terms of the delay and cost, we propose a distributed coalition formation algorithm to form the MPC group, in which each user has the choice to join or leave the group. Simulations show that the proposed algorithm establishes MPCs successfully, considering the trade-off between the payoff gain and the MPC delay cost.

Balanced Off-Chain Payment Channel Network Routing Strategy Based On Weight Calculation

AbstractOff-chain payment channel network is an effective solution to deal with low throughput and high load on the blockchain. However, the quality of the routing scheme will directly affect the performance of the off-chain network system. Existing routing schemes cannot select an appropriate transaction path according to the actual needs of user nodes. And they may also lead to network congestion, channel imbalance and node centralization. This paper proposes a new balanced routing selection scheme based on weight calculation called BRBW, which builds a weight model by Analytic Hierarchy Process. BRBW comprehensively considers the channel capacity, handling fee and path length to improve transaction success rate, reduce channel congestion and maintain the long-term sustainability of the payment channel. It uses a modified maximum flow algorithm to find paths with sufficient capacity and selects the transaction path for a large payment through linear programming. Finally, we do some experiments and compare with state-of-the-art approaches in the test environment. Simulation results show that under the same network environment, the payment success rate of the BRBW is above 75%, and the channel utilization rate is about 10% higher than other schemes.

CryptoMaze: Privacy-Preserving Splitting of Off-Chain Payments

Payment protocols developed to realize off-chain transactions in Payment channel network (PCN) assumes the underlying routing algorithm transfers the payment via a single path. However, a path may not have sufficient capacity to route a transaction. It is inevitable to split the payment across multiple paths. If we run independent instances of the protocol on each path, the execution may fail in some of the paths, leading to partial transfer of funds. A payer has to reattempt the entire process for the residual amount. We propose a secure and privacy-preserving payment protocol, CryptoMaze. Instead of independent paths, the funds are transferred from sender to receiver across several payment channels responsible for routing, in a breadth-first fashion. Payments are resolved faster at reduced setup cost, compared to existing state-of-the-art. Correlation among the partial payments is captured, guaranteeing atomicity. Further, two party ECDSA signature is used for establishing scriptless locks among parties involved in the payment. It reduces space overhead by leveraging on core Bitcoin scripts. We provide a formal model in the Universal Composability framework and state the privacy goals achieved by CryptoMaze. We compare the performance of our protocol with the existing single path based payment protocol, Multi-hop HTLC, applied iteratively on one path at a time on several instances. It is observed that CryptoMaze requires less communication overhead and low execution time, demonstrating efficiency and scalability.

Auto-tune: An efficient autonomous multi-path payment routing algorithm for Payment Channel Networks

Payment Channel Network (PCN) is a scaling solution for Cryptocurrency networks. We advance the PCN multi-path routing by better modeling the system and incorporating the cost of routing fee and the privacy requirement of the channel balance. We design an autonomous routing algorithm, Auto-Tune, optimizing the routing concerning the success rate and the routing fee and utilizing the limited channel capacity information. The simulation result shows a significant performance gain in the success rate of Auto-Tune over the current PCN implementation based on single-path routing. To analyze the performance cost from the system requirement of the channel balance privacy, we compare Auto-Tune against the state-of-the-art Flash algorithm assuming the availability of the channel-balance information (such channel-balance information violates the PCN privacy requirement and does not comply with the current PCN implementations and practices). The simulation results show that the success rate and fee obtained by Auto-Tune are close to that obtained by Flash (which achieves the optimal fee result by using the exact channel-balance information).

Enabling scalable and unlinkable payment channel hubs with oblivious puzzle transfer

Payment channel networks (PCNs) are effective techniques for extending the scalability of cryptocurrencies. It achieves this by establishing a direct off-line channel from the sender to the receiver, going through one intermediary (aka. the hubs). In such scenarios, the hubs know the origin and destination of each transaction flowing through them, which jeopardizes the privacy of the underlying systems. Unfortunately, former efforts in ensuring transaction unlinkability either rely on trusted mixing services, are inefficient constructed (e.g., constructed inefficient cryptographic primitives), or have limited applicability. In this paper, we present ObliHub, an efficient payment channel scheme that conceals transaction direction information to the hubs. The core technique of ObliHub in achieving unlinkability is our tailored oblivious puzzle transfer protocol (OPT), which enables puzzle solving among the payer, the hub, and the receiver to be conducted in an oblivious manner – the hub center neither knows where a puzzle hint came from nor who acquired it. The implementation of ObliHub only requires efficient cryptographic primitives, and compared with A2L (a state-of-the-art Bitcoin-compatible PCH using homomorphic encryption), ObliHub saves 0.2 seconds in computation time over previous solutions and improves transfer throughput. Besides, our scheme is in accord with Universal Composability (UC) framework and we provide a comprehensive security analysis of it.

An Architecture Using Payment Channel Networks for Blockchain-based Wi-Fi Sharing

Enabling Internet access while taking load of mobile networks, the concept of Wi-Fi sharing holds much potential. While trust-based concepts require a trusted intermediary and cannot prevent malicious behavior, for example, conducted through fake profiles, security-based approaches lack adequate accounting mechanisms and coverage. Against this backdrop, we develop a Wi-Fi sharing architecture based on blockchain technology and payment channel networks. Our contribution is twofold: First, we present a comprehensive collection of design principles for workable Wi-Fi sharing networks. Second, we propose and evaluate a reference architecture that augments current approaches with adequate accounting mechanisms and facilitates performance, scalability, security, and participant satisfaction.

LEAF: Let’s Efficiently Make Adaptive Forwarding in Payment Channel Networks

Blockchain technology is widely applicable to modern payment systems but has inherent throughput limitations. Off-chain networks are proposed to solve scalability issues, which allows parties to efficiently perform micropayments without committing all of the payments to the blockchain. Off-chain payments with security and privacy protection requirements use the smart contract to ensure security and reduce the risk of sensitive information leakage. Although off-chain payments avoid expensive on-chain operations, it raises many concerns, such as the capacity limitation of payment channels and highly dynamic channel status, lowering the throughput of payment channel networks (PCNs). In our work, we explore the path overlap issues in PCNs and propose a decentralized payment routing scheme to improve the network throughput and reduce the redundant traffic overhead of PCNs, thereby guaranteeing efficient payments. The simulation results indicate that the proposed routing algorithm can achieve higher throughput than other routing schemes while guaranteeing short payment times.

Multi-Party Payment Channel Network Based on Smart Contract

Blockchain-based cryptocurrencies are severely limited in transaction throughput and latency. A promising solution to this issue is a payment channel, which allows trust-free payments between two peers without exhausting the resources of the blockchain. A linked payment channel network (PCN) enables payments between two peers through a series of intermediate nodes that forward and charge for the payments. However, most of existing proposals only use the shortest path as the path of the transaction, which causes the frequently reused channels to be exhausted quickly. In addition, most of existing PCNs are almost only designed for payments between two parties, which leads to limited application scenarios. When multiple payments use the same intermediate channel, the two-party PCNs cannot achieve simultaneous payments. In this paper, we propose a multi-party payment channel (MPC) network, a payment channel proposal that supports multiple payments using the same intermediate channel simultaneously, thereby greatly expanding the application scenarios of payment channels. In addition, our channel selection and transaction conversion strategies can also increase the success rate of transactions. We implement MPC network in the simulated blockchain network and lightning network based on Truffle, and a large number of experiments verify the effectiveness of our solution.

Learning-Based Off-Chain Transaction Scheduling in Prioritized Payment Channel Networks

Payment channel network (PCN) is one of the promising solutions for scalable blockchains since it shows great potential in improving blockchain network throughput. However, the growing number of transactions and the payment-channel sharing of concurrent transactions can lead to channel congestion. Although many studies have proposed different solutions to solve this problem, they ignore a fact that applications may have different transaction rate requirements at different times. In this paper, we propose a priority-aware PCN to meet the requirements of those transactions. Senders in priority-aware PCNs can specify the priority of their transactions by paying a corresponding forwarding fee on each hop along the transaction path. However, capacity competition occurs on the shared hops. Moreover, we propose a multi-agent DQN-based priority assignment algorithm to address the competition issue and design a PCN simulator for performance evaluation. Simulation results show that our solution can guarantee a high throughput of transactions and assign priorities appropriately to balance the transaction rate and forwarding fee cost. The experimental results demonstrate that the priority scheduling scheme can achieve higher transaction throughput and success ratio than other scheduling methods in a congested PCN environment.

Compact Learning Model for Dynamic Off-Chain Routing in Blockchain-Based IoT

Dynamic off-chain routing in payment channel network (PCN)-based Internet of Things (IoT) is attracting increasing research attention. However, there are two major issues in dynamic routing in PCN-based IoT with resource-limited devices. The first issue is how to achieve high long-term transaction efficiency in PCN with dynamic channel capacities. The second issue is how to achieve a lightweight routing algorithm deployed on IoT devices while achieving high transaction efficiency, i.e., successful payment amount and success ratio. Therefore, in this paper, we propose a compact deep reinforcement learning (DRL) algorithm to learn the joint dynamic and lightweight routing policy for maximizing long-term transaction efficiency. To obtain optimal performance in dynamic routing problems for off-chain systems, a proximal policy optimization algorithm is employed to create an actor–critic learning structure for training the teacher DRL model. To obtain a compact and efficient student DRL model, an adaptive pruning technique is utilized for pruning unnecessary parameters of networks in the teacher model adaptively without affecting its learning ability. Furthermore, knowledge distillation is leveraged to improve the performance of the student network. Thus, a compact and efficient student DRL model can be developed and implemented to maximize the long-term transaction efficiency in off-chain systems on resource-limited IoT devices. The simulation results demonstrate that the proposed DRL algorithm outperforms the other baseline algorithms in PCN transaction efficiency while requiring only 10% of the computation and storage resources compared with that of the original teacher model.

Anti-Collusion Multiparty Smart Contracts for Distributed Watchtowers in Payment Channel Networks

Leveraging watchtowers to monitor payment channel networks (PCNs) is regarded to be a promising option to ensure off-chain transaction security and boost cryptocurrency scalability. However, existing solutions have two major limitations: First, since the watchtower’s inaction or collusion with counterparties, the deposits in off-chain transactions will be threatened; Second, due to occasional false positives, the efficiency of the single watchtower in monitoring the payment channels for fraud is questionable. To solve this, we present anti-collusion multiparty smart contracts for distributed watchtowers in PCNs. Specifically, we first design the distributed watchtower mechanism to solve the false positive problem in regulating PCNs. In addition, we utilize smart contracts to constrain and force counterparties to relinquish collusion in the distributed watchtower mechanism, thus making collusion impossible for rational parties. We further offer a mathematical proof and contract implementation in Solidity. Finally, extensive experiments and contracts executed on Ethereum under various benchmarks with baseline comparison demonstrate the validity of our proposals. Specifically, our scheme can both improve the throughput and accuracy by up to 20-25% and 10-15%, respectively, and reduce the false positive rate by up to 10% compared with existing single watchtower mechanism.

Blockchain-Empowered Efficient Data Sharing in Internet of Things Settings

Sharing data across various Internet of Things (IoT) devices has been a common challenge due to efficiency, security, and stability issues. Blockchain, with security features, is considered to be a potential solution for data sharing in IoT settings. However, traditional blockchain-based solutions cannot satisfy the efficiency requirement of high-frequency data sharing among IoT devices. In this paper, we propose an efficient IoT data sharing approach by adopting the Payment Channel Network (PCN)-extended blockchain. Besides, we develop a homomorphic hashing-based transaction segmentation scheme to solve the issue of low transaction success ratio caused by channel deposit restrictions in PCN. In addition, a Multi-point Relay (MPR)-based multi-path routing scheme has been developed to ensure high-frequency transaction forwarding. The communication overhead of maintaining the routing table is reduced by our proposed Multi-point Relay Selection algorithm, and multiple alternate paths generated by Multiple Routing Path algorithm can improve the transaction success rate. Experiment evaluations have demonstrated that that our proposed approach outperforms the baseline approaches in terms of the transaction efficiency and success ratio.

SorTEE: Service-Oriented Routing for Payment Channel Networks With Scalability and Privacy Protection

Payment channel networks (PCNs) are emerged as the most widely deployed solution to mitigate the scalability problem of permissionless cryptocurrencies, allowing vast payments to be carried out off-chain. Routing, which finds feasible paths between the senders and receivers, is critical for PCNs. However, existing solutions either fail to achieve high scalability that can maintain low storage/computation/network communication overhead, or they are susceptible to privacy disclosure. In this paper, we propose SorTEE, a service-oriented routing solution for PCNs, which adopts a set of service nodes to alleviate the per-user burden of routing and achieves more comprehensive privacy guarantees than the state-of-the-art by leveraging trusted execution environments (TEEs). SorTEE demands users communicate with the TEE by the secure channel to protect the privacy of transaction value. Then, an oblivious path mechanism is designed to construct redundant paths with the pseudo senders and receivers generated by TEEs to confuse its untrusted controller. Further, we report a novel attack that allows malicious service nodes to drop the valid paths for profit, and design a feedback mechanism to relieve it. Moreover, SorTEE hides the identities of the senders/receivers for the intermediate nodes of payment paths by introducing a novel identity information transfer scheme called encrypted identity chain. Based on security analysis and performance evaluation, our results demonstrate that SorTEE is able to achieve sufficient privacy-preserving payment and low per-user overhead.

Guest Editorial Special Issue on Intelligent Blockchain for Future Communications and Networking: Technologies, Trends, and Applications

Blockchain technology is becoming the cornerstone for the development and deployment of other technologies like Federated Learning (FL) and the Internet of Things (IoT), as it plays a critical role in data sharing and incentives. Blockchains supports decentralization, data-privacy protection, security, and reliability. Assuring secure data sharing in mobile computing and FL is challenging because of untrustworthy participants and unknown data quality. Blockchain provides trust in decentralized environments without requiring trusted third parties. By using smart contracts, blockchain has been able to supporting rich decentralized applications. However, the scalability of blockchain is a challenge that prevents its wide adoption by high-performance applications. To address the blockchain scalability issue, various blockchain sharding technologies and off-chain solutions have been proposed. To improve the network throughput, blockchain sharding divides the entire network into several smaller parallel groups and exploits fast consensus algorithms in blockchain shards. Off-chain solutions, such as payment channel networks (PCNs), transfer the slow on-chain transactions to the off-chain environment, in which transactions can be accelerated. Without consensus and on-chain expensive operations, off-chain scalable solutions significantly reduce transaction costs and increase transaction throughput. This special issue aims to provide a forum for the presentation of state-of-the-art research approaches that advance the construction of intelligent blockchain systems. A total of 27 articles were accepted after a two-round rigorous review process. Based on their topics, we have grouped the accepted articles into four categories: blockchain-based federated learning systems, blockchain and the IoT, blockchain scalability, and high-performance blockchains. In what follows, we introduce these articles and their contributions.

A Multipath Routing for Payment Channel Networks for Internet of Things Microtransactions

The blockchain with a distributed network structure can provide a reliable and secure environment for Internet of Things (IoT) transactions, which also suffers from low throughput, high computation overhead, and large transaction fee. Payment channel networks (PCNs) are developed to address the scalability issue of blockchain. A key enabler of PCNs is the path-finding services. Most of existing routing algorithms target at finding a single feasible path, which may lead to failure of large payments. Moreover, previous solutions did not consider the transaction fee of the chosen path, which is extremely important to cost-sensitive users. In this work, we design a new multipath routing algorithm for PCNs that aims at minimizing transaction fees. Together considering the determination of the optimal number of paths, the optimal path routes and the optimal allocation leads to difficulty of the problem. In addition, the transaction fees along the path are closely related to the amount of payment, and the capacity of a payment channel limits the payment that can be carried. To address these challenges, we propose MILPA-PCN, a cost-effective multipath routing framework for PCNs. We develop a genetic algorithm-based routing determination algorithm with carefully designed genetic operations. We evaluate MILPA-PCN based on the real trace of the lightning network, and verify that MILPA-PCN can reduce the transaction fee by 33.56% and improve the payment success rate by 14.45%.

Improving transaction succeed ratio in payment channel networks via enhanced node connectivity and balanced channel capacity

Payment channel networks (PCNs) are generally regarded as one of the most effective and promising scalability solutions for blockchain-based cryptocurrency systems, but suffer the issues of low success ratio and long confirmation latency in processing transactions. In this paper, we demonstrate the feasibility of tremendously increasing the success ratio of transactions and improving their execution efficiency by enhancing network nodes' connectivity and enforcing a balanced network channel capacity. To implement such ideas, multiple designs have been made. First, to extent nodes connectivity, we transform the nearly-linear ordered nodes into a star payment structured typology, and design an incentive financing mechanism to restructure a new landmark routing typology design. Especially, for the marginalized or dissociative nodes, we utilize specific financial loan strategies to encourage them to (re)join the system. Besides, we propose the Power Atomic Multi-Path Payments (Power AMP) traffic distribution method, which hierarchically allocates the bottleneck's currently-available capacity (to replace the random or equal division used in traditional AMP), and thus archives a balanced traffic usage. With such efforts, we improve the transaction success ratio and efficiency of transaction exertion by order of magnitude—compared with traditional PCN using the benchmark of landmark route, our method improves the success ratio by 11.06%.

Generous or Selfish? Weighing Transaction Forwarding Against Malicious Attacks in Payment Channel Networks

Generous or Selfish? Weighing Transaction Forwarding Against Malicious Attacks in Payment Channel Networks

MPCN-RP: A Routing Protocol for Blockchain-Based Multi-Charge Payment Channel Networks

MPCN-RP: A Routing Protocol for Blockchain-Based Multi-Charge Payment Channel Networks

Understanding the Benefit of Being Patient in Payment Channel Networks

Scaling blockchain efficiency is crucial to its widespread usage in which the payment channel is one of the most prominent approaches. With payment channels and the network they construct, two users can move some transactions off-chain to avoid expensive and time-consuming on-chain settlements. Existing works are devoted to designing high-throughput payment channel networks (PCNs) or efficient PCN routing policies to reduce the fee charged by intermediate nodes. In this paper, we investigate the PCN routing from a different perspective by answering whether the routing fee of transactions can be saved through being a bit more patient. The key idea is to reorder the processing sequence of atomic transactions, other than to handle each of them separately and immediately. We present two mechanisms, one is periodic transaction processing and the other is purely strategic waiting. In the former, the incoming transactions in a short time interval are processed collectively. We formulate an optimization model to minimize their total routing fee and derive the optimal permutation of processing transactions as well as the routing policy for each of them. A <i>Shapley value</i> based scheme is presented to redistribute the benefit of reordering among the transactions efficiently and fairly. In the latter, we model the waiting time of a strategic transaction on a single payment channel as the <i>first passage time</i> problem in queuing theory when the transaction value is higher than the channel balance upon its arrival. By capturing the balance dynamics, we are able to calculate the recursive expression of waiting time distribution that is useful to gauge a user&#x0027;s cost of patience. Experimental results manifest that our cost redistribution mechanism can effectively save routing fees for all the transactions, and the waiting time distribution coincides with the model well.