Settling Payments Fast and Private: Efficient Decentralized Routing for Path-Based Transactions

Roos, Stefanie; Moreno-Sanchez, Pedro; Kate, Aniket; Goldberg, Ian

doi:10.48550/arxiv.1709.05748

Cited by 25 publications

(37 citation statements)

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“…Multiple path-finding protocols have been proposed the the LN and payment channel networks in general, such as Flare [24], SpeedyMurmurs [26], SilentWhispers [14], and Spider [29,30]. A model for redundant parallel payments has been proposed as Boomerang [7].…”

Section: Related Workmentioning

confidence: 99%

Security and Privacy of Lightning Network Payments with Uncertain Channel Balances

Pickhardt¹,

Tikhomirov²,

Biryukov³

et al. 2021

Preprint

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Section: Related Workmentioning

confidence: 99%

Security and Privacy of Lightning Network Payments with Uncertain Channel Balances

Pickhardt¹,

Tikhomirov²,

Biryukov³

et al. 2021

Preprint

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“…Regardless of the primitive, a challenge all PCNs share is how to route transactions in the PCN while maintaining balanced channels for as long as possible. Classic routing studies in PCNs like SilentWhispers [19], SpeedyMurmurs [26], and others [25] ignore that channels may be slowly depleting. A promising approach to avoid channel depletion and prolong the network availability for transaction routing is to maintain balanced channels or occasionally perform rebalancing.…”

Section: Related Workmentioning

confidence: 99%

HIDE & SEEK: Privacy-Preserving Rebalancing on Payment Channel Networks

Avarikioti¹,

Pietrzak²,

Salem³

et al. 2021

Preprint

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Payment channels effectively move the transaction load off-chain thereby successfully addressing the inherent scalability problem most cryptocurrencies face. A major drawback of payment channels is the need to "top up" funds on-chain when a channel is depleted. Rebalancing was proposed to alleviate this issue, where parties with depleting channels move their funds along a cycle to replenish their channels off-chain. Protocols for rebalancing so far either introduce local solutions or compromise privacy. In this work, we present an opt-in rebalancing protocol that is both private and globally optimal, meaning our protocol maximizes the total amount of rebalanced funds. We study rebalancing from the framework of linear programming. To obtain full privacy guarantees, we leverage multi-party computation in solving the linear program, which is executed by selected participants to maintain efficiency. Finally, we efficiently decompose the rebalancing solution into incentive-compatible cycles which conserve user balances when executed atomically.

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“…Credit Network Performance. There has been substantial recent interest in quantifying credit network performance, broadly defined as transaction throughput or success rate; prior work has studied the impact of several categories of influencing factors, including routing and scheduling protocols [31,45,46,52,53], privacy constraints [30,40,49,50], defaulting agents [37], and network topology [12,15,16,23].…”

Section: Related Workmentioning

confidence: 99%

The Effect of Network Topology on Credit Network Throughput

Sivaraman,

Tang,

Venkatakrishnan

et al. 2021

Preprint

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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 random topologies as well as to aid in the synthesis of topologies robust to deadlocks.

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Settling Payments Fast and Private: Efficient Decentralized Routing for Path-Based Transactions

Cited by 25 publications

References 0 publications

Security and Privacy of Lightning Network Payments with Uncertain Channel Balances

Security and Privacy of Lightning Network Payments with Uncertain Channel Balances

HIDE & SEEK: Privacy-Preserving Rebalancing on Payment Channel Networks

The Effect of Network Topology on Credit Network Throughput

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