Welcome to the resource topic for 2022/317
Title:
Thora: Atomic And Privacy-Preserving Multi-Channel Updates
Authors: Lukas Aumayr, Kasra Abbaszadeh, Matteo Maffei
Abstract:Most blockchain-based cryptocurrencies suffer from a heavily limited transaction throughput, which is a barrier to their growing adoption. Payment channel networks (PCNs) are one of the most promising solutions to this problem. PCNs reduce the on-chain load of transactions and increase the throughput by processing many payments off-chain. In fact, any two users connected via a path of payment channels (i.e., joint addresses between the two channel end-points) can perform payments and the underlying blockchain is used only when there is a dispute between users. Unfortunately, payments in PCNs can only be conducted securely along a path, which prevents the design of many interesting applications. Moreover, the most widely used implementation, the Lightning Network in Bitcoin, suffers from a collateral lock time linear in the path length, it is affected by security issues, and it relies on specific scripting features called Hash Timelock Contracts that restricts its applicability. In this work, we present Thora, the first Bitcoin-compatible off-chain protocol that enables atomic multi-channel updates across generic topologies beyond paths. Thora allows payments through distinct PCNs sharing the same blockchain and enables new applications such as secure and trustless crowdfunding, mass payments, and channel rebalancing in off-chain ways. Our construction requires only constant collateral and no specific scripting functionalities other than digital signatures and timelocks, thereby being applicable to a wider range of blockchains. We formally define security and privacy in the Universal Composability framework and show that our cryptographic protocol is a realization thereof. In our performance evaluation we show that our construction requires constant collateral, is independent of the number of channels, and has only a moderate off-chain communication as well as computation overhead.
ePrint: https://eprint.iacr.org/2022/317
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