In this work, we reexamine the vulnerability of Payment Channel Networks (PCNs) to bribing attacks, where an adversary incentivizes blockchain miners to deliberately ignore a specific transaction to undermine the punishment mechanism of PCNs. While previous studies have posited a prohibitive cost for such attacks, we show that this cost can be dramatically reduced (to approximately $125), thereby increasing the likelihood of these attacks. To this end, we introduce Bribe & Fork, a modified bribing attack that leverages the threat of a so-called feather fork which we analyze with a novel formal model for the mining game with forking. We empirically analyze historical data of some real-world blockchain implementations to evaluate the scale of this cost reduction. Our findings shed more light on the potential vulnerability of PCNs and highlight the need for robust solutions.

We revisit the severely limited throughput problem of cryptocurrencies and propose a novel rebalancing approach for Payment Channel Networks (PCNs). PCNs are a popular solution for increasing the blockchain throughput, however, their benefit depends on the overall users' liquidity. Rebalancing mechanisms are the state-of-the-art approach to maintaining high liquidity PCNs. However, existing opt-in rebalancing mechanisms exclude users that may assist in rebalancing for small service fees, leading to suboptimal solutions and under-utilization of the PCNs' bounded liquidity.We introduce the first rebalancing approach for PCNs that includes all users, following a "all for one and one for all" design philosophy that yields optimal throughput. The proposed approach introduces a double-auction rebalancing problem, which we term Musketeer, where users can participate as buyers (paying fees to rebalance) or sellers (charging fees to route transactions). The desired properties are tailored to the unique characteristics of PCNs, including the novel game-theoretic property of cyclic budget balance that is a stronger variation of strong budget balance.Basic results derived from auction theory, including an impossibility and multiple mechanisms that either achieve all desiderata under a relaxed model or sacrifice one of the properties, are presented. We also propose a novel mechanism that leverages time delays as an additional cost to users. This mechanism is provably truthful, cyclic budget balanced, individually rational and economic efficient but only with respect to liquidity.

In this work, we revisit the severely limited throughput problem of cryptocurrencies and propose a novel rebalancing approach for Payment Channel Networks (PCNs). PCNs are a popular solution for increasing the blockchain throughput, however, their benefit depends on the overall users’ liquidity. Rebalancing mechanisms are the state-of-the-art approach to maintaining high liquidity in PCNs. However, existing opt-in rebalancing mechanisms exclude users that may assist in rebalancing for small service fees, leading to suboptimal solutions and under-utilization of the PCNs’ bounded liquidity. We introduce the first rebalancing approach for PCNs that includes all users, following a “all for one and one for all” design philosophy that yields optimal throughput. The proposed approach introduces a double-auction rebalancing problem, which we term Musketeer, where users can participate as buyers (paying fees to rebalance) or sellers (charging fees to route transactions). The desired properties tailored to the unique characteristics of PCNs are formally defined, including the novel game-theoretic property of cyclic budget balance that is a stronger variation of strong budget balance. Basic results derived from auction theory, including an impossibility and multiple mechanisms that either achieve all desiderata under a relaxed model or sacrifice one of the properties, are presented. We also propose a novel mechanism that leverages time delays as an additional cost to users. This mechanism is provably truthful, cyclic budget balanced, individually rational and economic efficient but only with respect to liquidity.

Cross-chain communication is instrumental in unleashing the full potential of blockchain technologies, as it allows users and developers to exploit the unique design features and the profit opportunities of different existing blockchains. The majority of interoperability solutions are provided by centralized exchanges and bridge protocols based on a trusted majority, both introducing undesirable trust assumptions compared to native blockchain assets. Hence, increasing attention has been given to decentralized solutions: Light and super-light clients paved the way for chain relays, which allow verifying on a blockchain the state of another blockchain by respectively verifying and storing a linear and logarithmic amount of data. Unfortunately, relays turn out to be inefficient in terms of computational costs, storage, or compatibility. We introduce Glimpse, an on-demand bridge that leverages a novel on-demand light client construction with only constant on-chain storage, cost, and computational overhead. Glimpse is expressive, enabling a plethora of DeFi and off-chain applications such as lending, pegs, proofs of oracle attestations, and betting hubs. Glimpse also remains compatible with blockchains featuring a limited scripting language such as the Liquid Network (a pegged sidechain of Bitcoin), for which we present a concrete instantiation. We prove Glimpse security in the Universal Composability (UC) framework and further conduct an economic analysis. We evaluate the cost of Glimpse for Bitcoin-like chains: verifying a simple transaction has at most 700 bytes of on-chain overhead, resulting in a one-time fee of $3, only twice as much as a standard Bitcoin transaction.

Sharding distributed ledgers is a promising on-chain solution for scaling blockchains but lacks formal grounds, nurturing skepticism on whether such complex systems can scale blockchains securely. We fill this gap by introducing the first formal framework as well as a roadmap to robust sharding. In particular, we first define the properties sharded distributed ledgers should fulfill. We build upon and extend the Bitcoin backbone protocol by defining consistency and scalability. Consistency encompasses the need for atomic execution of cross-shard transactions to preserve safety, whereas scalability encapsulates the speedup a sharded system can gain in comparison to a non-sharded system. Using our model, we explore the limitations of sharding. We show that a sharded ledger with n participants cannot scale under a fully adaptive adversary, but it can scale up to m shards where $$n=c'm\log m$$, under an epoch-adaptive adversary; the constant $$c'$$ encompasses the trade-off between security and scalability. This is possible only if the sharded ledgers create succinct proofs of the valid state updates at every epoch. We leverage our results to identify the sufficient components for robust sharding, which we incorporate in a protocol abstraction termed Divide & Scale. To demonstrate the power of our framework, we analyze the most prominent sharded blockchains (Elastico, Monoxide, OmniLedger, RapidChain) and pinpoint where they fail to meet the desired properties.

We introduce FnF-BFT, the first partially synchronous BFT protocol with performance guarantees under truly byzantine attacks during stable networking conditions. At its core, FnF-BFT parallelizes the execution of requests by allowing all replicas to act as leaders independently. Leader parallelization distributes the load over all replicas. Consequently, FnF-BFT fully utilizes all correct replicas’ processing power and increases throughput by overcoming the single-leader bottleneck. We prove lower bounds on FnF-BFT ’s efficiency and performance in synchrony: the amortized communication complexity is linear in the number of replicas and thus competitive with state-of-the-art protocols; FnF-BFT ’s amortized throughput with less than $$\frac{1}{3}$$ byzantine replicas is at least $$\frac{16}{27}$$ th of its best-case throughput. We also provide a proof-of-concept implementation and preliminary evaluation of FnF-BFT.

Off-chain protocols constitute one of the most promising approaches to solve the inherent scalability issue of blockchain technologies. The core idea is to let parties transact on-chain only once to establish a channel between them, leveraging later on the resulting channel paths to perform arbitrarily many peer-to-peer transactions off-chain. While significant progress has been made in terms of proof techniques for off-chain protocols, existing approaches do not capture the game-theoretic incentives at the core of their design, which led to overlooking significant attack vectors like the Wormhole attack in the past. In this work we take a first step towards a principled game-theoretic security analysis of off-chain protocols by introducing the first game-theoretic model that is expressive enough to reason about their security. We advocate the use of Extensive Form Games (EFGs) and introduce two instances of EFGs to capture security properties of the closing and the routing of the Lightning Network. Specifically, we model the closing protocol, which relies on punishment mechanisms to disincentivize parties to upload old channel states on-chain. Moreover, we model the routing protocol, thereby formally characterizing the Wormhole attack, a vulnerability that undermines the fee-based incentive mechanism underlying the Lightning Network.

Payment channel networks (PCNs) are a promising solution to the scalability problem of cryptocurrencies. Any two users connected by a payment channel in the network can theoretically send an unbounded number of instant, costless transactions between them. Users who are not directly connected can also transact with each other in a multi-hop fashion. In this work, we study the incentive structure behind the creation of payment channel networks, particularly from the point of view of a single user that wants to join the network. We define a utility function for a new user in terms of expected revenue, expected fees, and the cost of creating channels, and then provide constant factor approximation algorithms that optimise the utility function given a certain budget. Additionally, we take a step back from a single user to the whole network and examine the parameter spaces under which simple graph topologies form a Nash equilibrium.

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.

As the Bitcoin mining landscape becomes more competitive, analyzing potential attacks under the assumption of rational miners becomes increasingly relevant. In the rational setting, blockchain users can bribe miners to reap an unfair benefit. Established protocols such as Duplex Micropayment Channels and Lightning Channels are susceptible to bribery, which upends their financial guarantees. Indeed, we prove that in a two-party contract in which the honest party can spend an output right away, whereas the malicious can only spend the same output after a timelock, the latter party can promise a high fee to the miners, who then intentionally ignore the transaction of the honest party in anticipation of the higher fee. This effectively prevents a valid transaction from ever entering the blockchain, resulting in potentially severe financial losses for the honest and considerable gains for the malicious party. We expand previous results on timelock bribes to more realistic blockchains, proving that a general class of contracts are susceptible. We then apply our results to Duplex Micropayment Channels and Lightning Channels, providing exact bounds on their safe operating region. Furthermore, we enhance the Bitcoin Script of Duplex Micropayment Channels so that the coins of a party that attempts to bribe are given to the miners as fees, therefore effectively disincentivizing bribes. Our solution, named Suborn channels, is implemented as a proof-of-concept. We also propose a small change to Lightning Channels that achieves a similar effect. Moreover, we formally express the exact circumstances under which our two proposals ensure alignment of miner incentives with the prescribed protocol outcome.

Payment channel networks (PCNs) are one of the most prominent solutions to the limited transaction throughput of blockchains. Nevertheless, PCNs suffer themselves from a throughput limitation due to the capital constraints of their channels. A similar dependence on high capital is also found in inter-bank payment settlements, where the so-called netting technique is used to mitigate liquidity demands. In this work, we alleviate this limitation by introducing the notion of transaction aggregation: instead of executing transactions sequentially through a PCN, we enable senders to aggregate multiple transactions and execute them simultaneously to benefit from several amounts that may "cancel out". Two direct advantages of our proposal is the decrease in intermediary fees paid by senders as well as the obfuscation of the transaction data from the intermediaries. We formulate the transaction aggregation as a computational problem, a generalization of the Bank Clearing Problem. We present a generic framework for the transaction aggregation execution, and thereafter we propose Wiser as an implementation of this framework in a specific hub-based setting. To overcome the NP-hardness of the transaction aggregation problem, in Wiser we propose a fixed-parameter linear algorithm for a special case of transaction aggregation as well as the Bank Clearing Problem. Wiser can also be seen as a modern variant of the Hawala money transfer system, as well as a decentralized implementation of the overseas remittance service of Wise.