arXiv subjects

Lukas Aumayr

Publications and source records attributed to Lukas Aumayr.

2 recordsLinked to original sources

arXiv2026

Optimal Reward Allocation via Proportional Splitting

Following the publication of Bitcoin's arguably most famous attack, selfish mining, various works have introduced mechanisms to enhance blockchain systems' game-theoretic resilience. The only proof-of-work reward rule with a Nash-equilibrium guarantee, FruitChains, demands reward finality on the order of days. The rules that settle in minutes have no such guarantee, and one of them, Reward Splitting, still outperforms FruitChains on most of the metrics that matter in deployment. This paper closes that gap between theory and practice. We introduce FairChain, a two-level transformation for any proof-of-work Nakamoto-style protocol. At the protocol layer, FairChain records low-difficulty samples called workshares alongside blocks. At the reward layer, it applies Proportional Reward Splitting (PRS): each height's reward is divided among the competing work objects in proportion to the intrinsic work behind them, with workshares supplying a fresh power estimate at every height. The fork-choice rule and block-production loop are left untouched, so the host chain's security carries over unchanged. Workshares can be discarded once the corresponding rewards mature, leaving zero on-chain footprint. We prove FairChain is a \r{ho}-coalition-safe ε-Nash equilibrium for sufficiently large parameters, matching FruitChains in theory. To evaluate practical performance, we leverage Markov decision processes and compute the optimal adversarial policy under each utility function, rather than the gain of any one attack. At a six-block confirmation window, FairChain raises the deviation threshold to 38% of mining power and beats every mechanism in that framework on incentive compatibility, subversion gain (except FruitChains above 42%), and censorship susceptibility (except FruitChains below 25%).

cs.GT↗

arXiv2024

Optimizing Virtual Payment Channel Establishment in the Face of On-Path Adversaries

Payment channel networks (PCNs) are among the most promising solutions to the scalability issues in permissionless blockchains, by allowing parties to pay each other off-chain through a path of payment channels (PCs). However, routing transactions comes at a cost which is proportional to the number of intermediaries, since each charges a fee for the routing service. Furthermore, analogous to other networks, malicious intermediaries in the payment path can lead to security and privacy threats. Virtual channels (VCs), i.e., bridges over PC paths, mitigate the above PCN issues, as an intermediary participates only once to set up the VC and is then excluded from every future VC transaction. However, similar to PCs, creating a VC has a cost that must be paid out of the bridged PCs' balance. Currently, we are missing guidelines to where and how many VCs to set up. Ideally, VCs should minimize transaction costs while mitigating security and privacy threats from on-path adversaries. In this work, we address for the first time the VC setup problem, formalizing it as an optimization problem. We present an integer linear program (ILP) to compute the globally optimal VC setup strategy in terms of transaction costs, security, and privacy. We then accompany the computationally heavy ILP with a fast local greedy algorithm. Our model and algorithms can be used with any on-path adversary, given that its strategy can be expressed as a set of corrupted nodes that is estimated by the honest nodes. We conduct an evaluation of the greedy algorithm over a snapshot of the Lightning Network (LN), the largest Bitcoin-based PCN. Our results confirm on real-world data that our greedy strategy minimizes costs while protecting against security and privacy threats of on-path adversaries. These findings may serve the LN community as guidelines for the deployment of VCs.

cs.CR↗