Payment Channel Networks Research Articles

Hiding payments in lightning network with approximate differentially private payment channels

Payment Channel Networks (PCN) form a class of techniques created to solve the scalability problems that permissionless blockchains such as Bitcoin face. The Lightning Network (LN) is the biggest PCN to date. As of September 2021, LN is an interconnected network with 27 thousand nodes and 71 thousand channels, representing a total payment capacity of 2500 bitcoins (114M USD). LN nodes establish trustless payment channels by locking funds in a funding transaction. The distribution of the funds can be modified with near-instant transactions without broadcasting them to the blockchain. By chaining channels together, it is possible for two nodes to transact without having a direct channel between them. This results in far fewer transactions needing to be broadcast and a theoretical number of off-chain transactions per second that can rival established, centralized payment systems. With off-chain transactions one can obtain more privacy than with on-chain transaction: individual payments through LN should be able to stay private. However, recent research suggests that this is not the case. Several works have shown that channel balances can be tracked with the Balance Discovery Attack (BDA). Monitoring the changes in channel balances over time enables an adversary to track individual payments in LN. Since payments can travel through channels outside the scope of influence of the payer or the payee, these BDA’s form an Interdependent Privacy (IDP) risk for the participants of the LN network. This work applies approximate differential privacy to hide payment amounts of a realistic size in LN. To our knowledge, this is the first time that strong privacy guarantees in the sense of approximate differential privacy are achieved in the setting of LN. We prove the feasibility with our prototype, compatible with LN today. Our solution is evaluated in terms of cost and utility and we show that the latter is not affected by our solution.

Apples and Oranges: On How to Measure Node Centrality in Payment Channel Networks

Apples and Oranges: On How to Measure Node Centrality in Payment Channel Networks

Retracted] Research on Multiparty Payment Technology Based on Blockchain and Smart Contract Mechanism

As a peer‐to‐peer “P2P” distributed ledger, the blockchain has the advantages of decentralization, no trust, open autonomy, and nontampering. Therefore, many users are willing to conduct transactions in blockchain cryptocurrency systems such as Bitcoin and Ethereum. However, the throughput of traditional blockchain is extremely low, and the transaction is so delayed. The payment channel network is the most promising solution to expand the blockchain for widespread use. Achieving secure instant payment on the payment channel can significantly increase transaction throughput and reduce transaction delays. When the payment channel is closed, the balance in the channel will be returned to an account on the blockchain. In this paper, we discuss the design and the implementation of a multiparty payment channel network based on smart contracts. Where a two‐party payment channel is designed based on blockchain and smart contracts, a new multiparty payment channel is established on the basis of the payment channel. A detailed definition and description are given, and the creation, update, and closing functions of the multiparty payment channel are designed. Moreover, we design a multiparty payment channel smart contract, deploy it to the local private blockchain, and conduct simulation and testing. The delay time of different transaction methods is counted, and the network topology type, transaction amount, and other factors are studied. The impact of transaction success rate and the gas consumption of different transaction methods are analyzed through multiple sets of experimental statistics.

The effect of network topology on credit network throughput

Credit networks rely on decentralized, pairwise trust relationships (channels) to exchange money or goods. Credit networks arise naturally in many financial systems, including the recent construct of payment channel networks in blockchain systems. An important performance metric for these networks is their transaction throughput. However, predicting the throughput of a credit network is nontrivial. Unlike traditional communication channels, credit channels can become imbalanced; they are unable to support more transactions in a given direction once the credit limit has been reached. This potential for imbalance creates a complex dependency between a network’s throughput and its topology, path choices, and the credit balances (state) on every channel. Even worse, certain combinations of these factors can lead the credit network to deadlocked states where no transactions can make progress. In this paper, we study the relationship between the throughput of a credit network and its topology and credit state. We show that the presence of deadlocks completely characterizes a network’s throughput sensitivity to different credit states. Although we show that identifying deadlocks in an arbitrary topology is NP-hard, we propose a peeling algorithm inspired by decoding algorithms for erasure codes that upper bounds the severity of the deadlock. We use the peeling algorithm as a tool to compare the performance of different topologies as well as to aid in the synthesis of topologies robust to deadlocks.

Formula omitted]-HTLC: Neo hashed time-Lock commitment to defend against wormhole attack in payment channel networks

In today’s cryptocurrency, the Payment Channel Network (PCN) is noticed as one of the most gifted off-chain solutions for scalability issues. Besides this, it consumes lesser transaction fees and low transaction confirmation time. However, security and privacy issues need to be addressed appropriately to make the solution even more effective. Most of the existing HTLC (Hashed Time-Lock Contract) protocols revealed the sender’s information to the intermediate users in the payment route. In this work, we propose an effective secure and privacy-preserving Payment Channel Network protocol, named Neo Hashed Time-Lock Commitment (n-HTLC) protocol. (n-HTLC) does not require the sender to send any information to each intermediate user along the payment route, thus preserves the identity of the sender. But, (n-HTLC) is not compatible with Sphinx onion packet format. Therefore, a symmetric key encryption-based protocol called kTLC has been proposed. kTLC is compatible with the Sphinx onion packet format, which is used in the current Lightning network atop of the Bitcoin blockchain network. The security of both n-HTLC and kTLC are proved using the Universal Composability (UC) framework. It is observed that both ensure that no attacker can extract information on the payment route if at least one of the users in the path is honest. To analyze the performance of both n-HTLC and kTLC payment protocol, we conduct experiments using the snapshots of Ripple network11According to the network snapshots (Dec 2015) Malavolta et al. (2017a) pre-processed by Mazumdar and Ruj (2020)., Lightning network22According to the network snapshots (May 2020) Lns pre-processed by Mazumdar and Ruj (2020)., and synthetic network of Mazumdar and Ruj (2020). Our experimental results show that both n-HTLC and kTLC outperform state-of-the-art off-chain payment protocols in terms of computational and communication overhead.

RobustPay+: Robust Payment Routing With Approximation Guarantee in Blockchain-Based Payment Channel Networks

The past decade has witnessed an explosive growth in cryptocurrencies, but the blockchain-based cryptocurrencies have also raised many concerns, among which a crucial one is the scalability issue. Suffering from the large overhead of global consensus and security assurance, even the leading cryptocurrencies can only handle up to tens of transactions per second, which largely limits their applications in real-world scenarios. Among many proposals to improve the cryptocurrency scalability, one of the most promising and mature solutions is the payment channel network (PCN), which offers the off-chain settlement of transactions with minimal involvement of expensive blockchain operations. However, transaction failures may occur due to external attacks or unexpected conditions, e.g., an uncooperative user becoming unresponsive. In this paper, we present a distributed robust payment routing protocol RobustPay <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> to resist transaction failures, which achieves robustness, efficiency, distributedness and approximate optimization. Specifically, we investigate the problem of robust routing in PCNs from an optimization perspective, which is to find a pair of payment paths for a payment request, while minimizing the worst-case transaction fee, subject to the timeliness and feasibility constraints. We present a distributed 2-approximation algorithm for this problem. Moreover, we modify the original Hashed Time-lock Contract (HTLC) protocol and adapt it to the robust payment routing protocol to achieve robustness and efficiency. Extensive simulations demonstrate that RobustPay <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> significantly outperforms baseline algorithms in terms of the success ratio and the average accepted value.

An evaluation of cryptocurrency payment channel networks and their privacy implications

Cryptocurrencies redefined how money can be stored and transferred among users. However, public blockchain-based cryptocurrencies suffer from high transaction waiting times and fees. To address these challenges, the payment channel network concept is touted as the most viable solution to be used for micro-payments. The idea is exchanging the ownership of money by keeping the state of the accounts locally which provides transaction approvals in seconds. Such attention on payment channel networks has inspired many recent studies that focus on how to design them and allocate channels such that the transactions will be secure and efficient. However, as payment channel networks are emerging and reaching a large number of users, privacy issues are becoming more relevant, this raises concerns about exposing not only individual habits but also businesses' revenues. In this paper, we first propose a categorization of the existing payment networks formed on top of blockchain-backed cryptocurrencies. After discussing several emerging attacks on user/business privacy in these payment channel networks, we qualitatively evaluate them based on a number of privacy metrics that relate to our case. Based on the discussions on the strengths and weaknesses of the approaches, we offer possible directions for research for the future of privacy based payment channel networks.

Cryptocurrency Solutions to Enable Micropayments in Consumer IoT

The successful amalgamation of cryptocurrency and consumer Internet-of-Things (IoT) devices can pave the way for novel applications in machine-to-machine economy. However, the lack of scalability and heavy resource requirements of initial blockchain designs hinder the integration, and it is unclear how consumer devices will be adopting cryptocurrency. Therefore, in this article, we critically review the existing integration approaches and cryptocurrency designs that strive to enable micropayments among consumer devices. We identify and discuss solutions under three main categories; direct integration, payment channel network, and new cryptocurrency design. The first approach utilizes a full node to interact with the payment system. Offline channel payment is suggested as a second-layer solution to solve the scalability issue and enable instant payment with low fee. New designs converge to semicentralized scheme and focus on lightweight consensus protocol that does not require high computation power. We evaluate the pros and cons of each of these approaches and then point out future research challenges. Our goal is to help researchers and practitioners to better focus their efforts to facilitate micropayment adoptions.

A scalable private Bitcoin payment channel network with privacy guarantees

While Bitcoin heavily dominates the cryptocurrency markets, its use in micropayments is still a challenge due to long transaction confirmation times and high fees. Recently, the concept of off-chain transactions is introduced that led to the idea of establishing a payment channel network called Lightning Network (LN), which utilizes multi-hop payments. Off-chain links provide the ability to make instant payments without a need to writing to Blockchain. However, LN's design still favors fees, and it is creating hub nodes or relays that defeat the purpose of Blockchain. In addition, it is still not reliable, as not all transactions are guaranteed to be delivered to their destinations. These issues hinder its wide adoption by retailers. To address this issue, in this paper, we argue that the retailers could create a private payment channel network among them to serve their business needs, just like the concept of private Blockchains. The goal is to build a pure peer-to-peer topology that will prevent the formation of hub nodes while also eliminating the need for any relays to increase the robustness of the payments. Assuming off-chain links as edges and retailers as nodes, we formulate the problem as a multi-flow commodity problem where transactions represent the commodities from various sources to destinations. As the multi-flow commodity problem is NP-Complete, we propose a heuristic approach that utilizes Dijkstra's shortest path algorithm for payments in a dynamic way by updating the edge weights when new paths need to be found. The order of transactions is randomized to provide fairness among the retailers. We further extend this approach to guarantee the privacy of payments by forcing all the payments to travel at least three hops. We utilized k-shortest path algorithm to choose from k options that will meet our criterion. The evaluations indicate that the proposed heuristic comes close to an optimal solution while providing scalability and guaranteeing user privacy.

Characterizing Performance Limits in Payment Channel Networks

With their instant transaction confirmation and high scalability, payment channel networks (PCNs), running off-chain and in parallel with blockchain systems, have recently attracted a substantial amount of research attention. It has been shown that there exists a significant gap between the theoretically optimal performance and the performance achievable given the stringent privacy requirements in practice. However, it remains unclear what the fundamental performance limits and key factors involved are, which turns out to be a challenging problem due to the unique characteristics in PCNs. In this paper, we, for the first time, develop a mathematical model capturing the PCN performance, and examine the impact from a number of factors including channel capacity and transactions. We are articularly interested in obtaining the gap between the theoretically optimal performance and the performance achievable in practice, which characterizes the design space in PCNs for scheduling transactions. Specifically, we derive how different transactions and channel capacities affect the PCN performance and the performance gap. Our analytical characterization of PCNs offers an in-depth understanding on their fundamental trade-off, and provides important insights on the design of PCNs.

Improving transaction success rate in cryptocurrency payment channel networks

Blockchain-based cryptocurrencies has received a substantial interest in the last decade as Blockchain can ensure trust among users without relying on third parties. However, cryptocurrency adoption for micro-payments has been limited due to slow confirmation of transactions and unforeseeable high fees, especially in the case of Bitcoin. To this end, creating off-chain payment channels between users is proposed which enables instant and nearly free transactions without writing to blockchain. Off-chain channel idea is then extended to establish payment channel networks to scale the idea to allow payment routing among many users. However, due to the way these channels are designed, both sides of a channel have a fixed one-way capacity for making transactions. Consequently, if one side consumes the whole one-way capacity, the channel becomes non-transitive in that particular direction, which causes failures of payments that would like to pass through. Eventually, the network becomes partitioned with unevenly distributed funds. In this paper, we propose the adoption of three specific techniques that aim to increase the overall success rate of payments and address channel imbalance problem to keep the payment channel network sustainable in the long run. First, we show the effectiveness of balance-aware routing that better utilizes available funds in the channels. Second, we propose an efficient method for selection of the gateway (i.e., connection point) for a user by considering the gateway’s inbound and outbound capacity. It exploits the fact that end-users can connect the network through multiple gateways any of which can be used to initiate the payment. Finally, we propose proportional payment splitting method to further increase success rate especially for large transactions. We implemented the three approaches for assessing their effectiveness. Compared to existing approaches such as maximum flow or greedy, the proposed approaches can achieve much higher success rates with channels balanced better.

A Cryptoeconomic Traffic Analysis of Bitcoin’s Lightning Network

Lightning Network (LN) is designed to amend the scalability and privacy issues of Bitcoin. It is a payment channel network where Bitcoin transactions are issued off the blockchain and onion routed through a private payment path with the aim to settle transactions in a faster, cheaper, and more private manner, as they are not recorded in a costly-to-maintain, slow, and public ledger. In this work, we design a traffic simulator to empirically study LN’s transaction fees and privacy provisions. The simulator relies only on publicly available data of the network structure and capacities, and generates transactions under assumptions that we attempt to validate based on information spread by certain blog posts of LN node owners.Our findings on the estimated revenue from transaction fees are in line with the widespread opinion that participation is economically irrational for the majority of the large routing nodes who currently hold the network together. Either traffic or transaction fees must increase by orders of magnitude to make payment routing economically viable. We give worst-case estimates for the potential fee increase by assuming strong price competition among the routers. We also estimate how current channel structures and pricing policies respond to a potential increase in traffic, how reduction in locked funds on channels would affect the network, and show examples of nodes who are estimated to operate with economically feasible revenue.Our second set of findings considers privacy. Even if transactions are onion routed, strong statistical evidence on payment source and destination can be inferred, as many transaction paths only consist of a single intermediary by the side effect of LN’s small-world nature. Based on our simulation experiments, we (1) quantitatively characterize the privacy shortcomings of current LN operation, and (2) propose a method to inject additional hops in routing paths to demonstrate how privacy can be strengthened with very little additional transactional cost.

Privacy-Utility Tradeoffs in Routing Cryptocurrency over Payment Channel Networks

As the adoption of cryptocurrencies grows to unprecedented levels, the scalability of blockchain technologies has become increasingly important. A major open question is whether cryptocurrencies are fundamentally able to support as much traffic as traditional, centralized solutions. One prominent approach for improving the scalability of blockchains is payment channel networks (PCNs) [1]. Instead of committing every transaction to the blockchain, a separate overlay network (called a PCN) is maintained. Each node represents a user, and each edge (or payment channel) represents pre-allocated funds that can be efficiently transacted between the two endpoints under a mutual agreement. Users can transact with each other by relaying payments over a sequence of channels.

Privacy-Utility Tradeoffs in Routing Cryptocurrency over Payment Channel Networks

Payment channel networks (PCNs) are viewed as one of the most promising scalability solutions for cryptocurrencies today. Roughly, PCNs are networks where each node represents a user and each directed, weighted edge represents funds escrowed on a blockchain; these funds can be transacted only between the endpoints of the edge. Users efficiently transmit funds from node A to B by relaying them over a path connecting A to B, as long as each edge in the path contains enough balance (escrowed funds) to support the transaction. Whenever a transaction succeeds, the edge weights are updated accordingly. In deployed PCNs, channel balances (i.e., edge weights) are not revealed to users for privacy reasons; users know only the initial weights at time 0. Hence, when routing transactions, users typically first guess a path, then check if it supports the transaction. This guess-and-check process dramatically reduces the success rate of transactions. At the other extreme, knowing full channel balances can give substantial improvements in transaction success rate at the expense of privacy. In this work, we ask whether a network can reveal noisy channel balances to trade off privacy for utility. We show fundamental limits on such a tradeoff, and propose noise mechanisms that achieve the fundamental limit for a general class of graph topologies. Our results suggest that in practice, PCNs should operate either in the low-privacy or low-utility regime; it is not possible to get large gains in utility by giving up a little privacy, or large gains in privacy by sacrificing a little utility.

Improving Success Rate of Transactions in Crypto currency Payment Channel Networks

In the recent times, the interest in Blockchainbased crypto currencies has increased rapidly. But the slow time of confirmation of transaction and the high charges per transaction prove to be a problem in the adoption of this technology for micro-payments. To solve this scalability problem, the idea of establishing payment channel networks where nodes establish payment channels between each other to perform offchain transactions. However, this method results in a certain one-way capacity for both nodes for making transactions which means that if a node exceeds its capacity, the channel becomes useless in that direction and hence the payment fails which creates an imbalance in the network. In this paper we aim to increase the rate of success of payments by using the fact that each node is connected to multiple points in a network which can act as gateways and we can use any of these gateways to complete a transaction. By using the inbound and outbound payment traffic ratio, we also define a method for the selection of a particular gateway to carry out the transaction. We use split payment capability with this method to increase the rate of success furthermore, especially for larger transactions.

Blockchain-Based Payment Channel Networks: Challenges and Recent Advances

Blockchain technology has been developed with the vision to enable trusted collaboration between untrusted parties, without the need for a central authority. Despite its many promising applications, the technology suffers from a scalability problem. In order to increase transaction throughput and decrease transaction confirmation latency, payment channel networks have been proposed. Payment channel networks introduce a layer on top of the main chain, in which transactions can happen in a safe manner between only the transacting parties without burdening the entire network. In this article, we present and highlight the many interesting research aspects this new type of network introduces. We first provide background on the mechanics of the operation of payment channel networks, and then proceed to present a plethora of research problems of networking and/or economics flavor arising in this context, including routing, scheduling, rebalancing, network design and topology analysis, and fee optimization. This work is within the scope of both the networking and network economics communities.

PayGo: Incentive-Comparable Payment Routing Based on Contract Theory

The increasing use of cryptocurrencies has raised scalability and performance issues in blockchain systems that require global consensus and security assurance for real-time payments. The payment channel network (PCN) is a promising off-chain solution for cryptocurrencies; in these networks, transactions are offloaded from the blockchain and are handled directly using a payment channel with minimum involvement of the blockchain. Routing protocols for PCNs are critical for finding a path-based transaction (PBT) path with low latency and high throughput, and several routing protocols have been proposed to address decentralization, concurrency, and privacy issues. However, incentive mechanisms for the payment service provider (PSP), which are critical for successful path-based transactions, have not been sufficiently studied. Previous routing protocols are proposed only to find a best path in the given budget. In this study, we propose the payGo routing protocol, which not only discovers a feasible path but also derives the optimal incentive for the PSP using contract theory. Furthermore, payGo induces the PSP to make a contract with a counterparty to guarantee payment latency and throughput with a penalty. We implement the payGo protocol by extending the Raiden network and evaluate its performance on a testbed. The results indicate that, in comparison with the conventional linear pricing for latency, payGo can improve about 90% of payer's utility by optimizing latency and incentive.

Blind Payment Protocol for Payment Channel Networks

Blind Payment Protocol for Payment Channel Networks

Hubs, Rebalancing and Service Providers in the Lightning Network

Payment channel networks are the most developed proposal to address the well-known issue of blockchain scalability. Currently, the Lightning Network (LN) is the mainstream and most used payment channel network. In a previous work we introduced CLoTH, a payment channel network simulator we developed to analyze capabilities and limitations of such networks. In this work, using CLoTH, we present results of three groups of simulations on a recent snapshot of the LN, aimed to shed a light on the following aspects. Firstly, we investigated how hubs influence the LN performance. Then, we analyzed the effectiveness of two different channel rebalancing approaches, an active and a passive one. Eventually, we studied performance of the LN when a few service-providers nodes receive payments from the other network nodes, which is a typical use case of payment channel networks. We found that the LN is resilient to the removal of hubs, that our passive rebalancing approach reduces of about one fourth the payments failures due to channel unbalancing, and that in the service-providers scenario a consistent part of payments fails because channels directing to the service providers become unbalanced. Our work contributes to prove the strengthen of the Lightning Network when removing hubs and its weakness in the service-provider scenario. In addition, the passive rebalancing approach proposed in this work is a good candidate for the implementation in the Lightning Network protocol to mitigate channel unbalancing.