Three Attacks on Proof-of-Stake Ethereum
Authors: Caspar Schwarz-Schilling, Joachim Neu, Barnabé Monnot, Aditya Asgaonkar, Ertem Nusret Tas, and David Tse
Recently, two attacks were presented against Proof-of-Stake (PoS) Ethereum: one where short-range reorganizations of the underlying consensus chain are used to increase individual validators’ profits and delay consensus decisions, and one where adversarial network delay is leveraged to stall consensus decisions indefinitely. We provide refined variants of these attacks, considerably relaxing the requirements on adversarial stake and network timing, and thus rendering the attacks more severe. Combining techniques from both refined attacks, we obtain a third attack which allows an adversary with vanishingly small fraction of stake and no control over network message propagation (assuming instead probabilistic message propagation) to cause even long-range consensus chain reorganizations. Honest-but-rational or ideologically motivated validators could use this attack to increase their profits or stall the protocol, threatening incentive alignment and security of PoS Ethereum. The attack can also lead to destabilization of consensus from congestion in vote processing.
HIDE & SEEK: Privacy-Preserving Rebalancing on Payment Channel Networks
Authors: Zeta Avarikioti, Krzysztof Pietrzak, Iosif Salem, Stefan Schmid, Samarth Tiwari, and Michelle Yeo
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.
zk-Fabric, a Polylithic Syntax Zero Knowledge Joint Proof System
Authors: Sheng Sun and Dr.Wen Tong
In this paper, we create a single-use and full syntax zero knowledge proof system, a.k.a zk-Fabric. Comparing with zk-SNARKS and another variant zero knowledge proofing system, zkBOO and it’s variant zkBOO++. We present multiple new approaches on how to use partitioned garbled circuits to achieve a joint zero-knowledge proof system, with the benefits of less overhead and full syntax verification. zk-Fabric based on partitioned garbled circuits has the advantage of being versatile and single use, meaning it can be applied to arbitrary circuits with more comprehensive statements, and it can achieve the non-interactivity among all participants. One of the protocols proposed within is used for creating a new kind of partitioned garbled circuits to match the comprehensive Boolean logical expression with multiple variables, we use the term “polythitic syntax” to refer to the context based multiple variables in a comprehensive statement. We also designed a joint zero knowledge proof protocol that uses partitioned garbled circuits.
RectorDApp: Decentralized Application for Managing University Rector Elections
Authors: Jesús Rosa-Bilbao and Juan Boubeta-Puiz
Blockchain is a distributed and secure database that can be applied to all types of transactions. Blockchain technology is growing in popularity because it allows for the development of applications whose information is traceable, immutable, transparent and reliable. Given the advantages that blockchain provides over other traditional systems, in this paper we present a decentralized application, called RectorDApp, for the management of university rector voting in a private, but transparent and immutable way, being able to verify publicly and in real time the election results. RectorDApp, capable of interacting with the Ethereum public blockchain network, was developed using the Truffle framework and the MetaMask software. The results demonstrate that RectorDApp is a highly useful application to address the digital transformation of university rector elections.
Discovery of Ethereum Topology Through Active Probing Approach
Authors: Soo Hoon Maeng, Meryam Essaid, Sejin Park, and Hongtaek Ju
The Ethereum network uses Kademlia, a well-known P2P network, which allows the search for new nodes, and the change of connection with neighboring nodes. The Ethereum network must cope with security attacks such as DDoS attacks, 51% attacks, and Sybil attacks, and scalability issues, which slows down the transaction processing speed per second (TPS) as the network expands. A deep analysis of the dynamically changing topology and the connection between the nodes constituting the topology is needed to solve these problems. Therefore, in this paper, we measure the topology in the Ethereum network using a passive probing data collection to search for active nodes in the network and an active probing method to check the activity of nodes participating in the Ethereum network. Our results give a clear insight into the topology properties and topology visualization.
On Designing Smart Agents for Service Provisioning in Blockchain-powered Systems
Authors: Naram Mhaisen, Mhd Saria Allahham, Amr Mohamed, Aiman Erbad, and Mohsen Guizani
Service provisioning systems assign users to service providers according to allocation criteria that strike an optimal trade-off between users Quality of Experience (QoE) and the operation cost endured by providers. These systems have been leveraging Smart Contracts (SCs) to add trust and transparency to their criteria. However, deploying fixed allocation criteria in SCs does not necessarily lead to the best performance over time since the blockchain participants join and leave flexibly, and their load varies with time, making the original allocation suboptimal. Furthermore, updating the criteria manually at every variation in the blockchain jeopardizes the autonomous and independent execution promised by SCs. Thus, we propose a set of light-weight agents for SCs that are capable of optimizing the performance. We also propose using online learning SCs, empowered by Deep Reinforcement Learning (DRL) agent, that leverage the chained data to continuously self-tune its allocation criteria. We show that the proposed learning-assisted method achieves superior performance on the combinatorial multi-stage allocation problem while still being executable in real-time. We also compare the proposed approach with standard heuristics as well as planning methods. Results show a significant performance advantage over heuristics and better adaptability to the dynamic nature of blockchain networks.