Each talk will be capped at 12 minutes, followed by a 3 minute Q&A.

<aside> 1️⃣ Banyan: Fast Rotating Leader BFT

Yann Vonlanthen, PhD @ ETH Zurich

We present Banyan, the first rotating leader state machine replication (SMR) protocol that allows transactions to be confirmed in a single round-trip time in the Byzantine fault tolerance (BFT) setting.

Our evaluation in a globally distributed wide-area network reveals that Banyan reduces latency by up to 30% compared to state-of-the-art protocols, without requiring additional security assumptions.

Paper: https://arxiv.org/abs/2312.05869

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<aside> 2️⃣ Agent-Based Modelling of Ethereum Consensus Benjamin Kraner, PhD @ UZH Zurich

We presents a study of the Poof-of-Stake (PoW) Ethereum consensus protocol, following the switch from Proof-of-Work (PoS) to Proof-of-Stake. The new protocol has resulted in reduced energy consumption and a shift in economic incentives, but it has also introduced new threat sources such as chain reorganizations and balancing attacks. Using a simple and flexible agent-based model, this study employs a time-continuous simulation algorithm to analyze the evolution of the blocktree and assess the impact of initial conditions on consensus quality. The model simulates validator node behavior and the information propagation throughout the peer-to-peer network of validators to analyze the resulting blockchain structure. Key variables in the model include the topology of the peer-to-peer network and average block and attestation latencies. Metrics to evaluate consensus quality are established, and means to observe the model's responsiveness to changes in parameters are provided. The simulations reveal a phase transition in which the system switches from a consensus state to a non-consensus state, with a theoretical justification presented for this observation.

Paper: https://ieeexplore.ieee.org/abstract/document/10174948

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<aside> 3️⃣ A Glimpse into Convex Agreement Diana Ghinea, PhD @ ETH Zurich

Achieving agreement among the parties in a distributed system is crucial for maintaining consistent views. This becomes particularly challenging due to the potential for parties’ failures, ranging from benign crashes to malicious (byzantine) behavior.

Byzantine Agreement is an extensively studied problem that tackles this challenge: it seeks to establish agreement on a value amongst a set of n parties even when up to t parties are byzantine. The value agreed upon needs to be meaningful and aligned with the honest parties' inputs. Standard Byzantine Agreement struggles to meet this requirement in real-world scenarios such as sensors trying to agree on some measurement, blockchain oracles trying to agree on the price of an asset, or robots trying to decide on a meeting point.

A stronger variant of Byzantine Agreement, called Convex Agreement, addresses this limitation by ensuring that the value agreed upon is somewhere in the honest inputs’ “range”, or in the honest inputs’ convex hull. In this talk, we will discuss new findings in the area of Convex Agreement.

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<aside> 4️⃣ Bitcoin user behaviour analysis based on balance data Yu Zhang, PhD @ UZH Zurich

When analyzing Bitcoin users' balance distribution, we observed that it follows a log-normal pattern. Drawing parallels from the successful application of Gibrat's law of proportional growth in explaining city size and word frequency distributions, we tested whether the same principle could account for the log-normal distribution in Bitcoin balances. However, our calculations revealed that the exponent parameters in both the drift and variance terms deviate slightly from one. This suggests that Gibrat's proportional growth rule alone does not fully explain the log-normal distribution observed in Bitcoin users' balances. During our exploration, we discovered an intriguing phenomenon: Bitcoin users tend to fall into two distinct categories based on their behavior, which we refer to as “poor” and “wealthy” users. Poor users, who initially purchase only a small amount of Bitcoin, tend to buy more bitcoins first and then sell out all their holdings gradually over time. The certainty of selling all their coins is higher and higher with time. In contrast, wealthy users, who acquire a large amount of Bitcoin from the start, tend to sell off their holdings over time. The speed at which they sell their bitcoins is lower and lower over time and they will hold at least a small part of their initial holdings at last. Interestingly, the wealthier the user, the larger the proportion of their balance and the higher the certainty they tend to sell. This research provided an interesting perspective to explore bitcoin users' behaviors which may apply to other finance markets.

Paper: https://arxiv.org/pdf/2409.10407

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