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 address the problem of detecting and punishing shareholder collusion in secret-sharing schemes. We do it in the recently proposed cryptographic model called individual cryptography (Dziembowski, Faust, and Lizurej, Crypto 2023), which assumes that there exist tasks that can be efficiently computed by a single machine but distributing this computation across multiple (mutually distrustful devices) is infeasible.

Adversarial social network analysis studies how graphs can be rewired or otherwise manipulated to evade social network analysis tools. While there is ample literature on manipulating simple networks, more sophisticated network types are much less understood in this respect. In this paper, we focus on the problem of evading FGA---an edge weight prediction method for signed weighted networks by Kumar et al. 2016. Among others, this method can be used for trust prediction in reputation systems. We study the theoretical underpinnings of FGA and its computational properties in terms of manipulability. Our positive finding is that, unlike many other tools, this measure is not only difficult to manipulate optimally, but also it can be difficult to manipulate in practice.

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.