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Maximal Extractable Value (MEV) refers to a wide class of economic attacks to public blockchains, where adversaries with the power to reorder, drop or insert transactions in a block can "extract" value from smart contracts. Empirical…
We present and validate a novel mathematical model of the blockchain mining process and use it to conduct an economic evaluation of the double-spend attack, which is fundamental to all blockchain systems. Our analysis focuses on the value…
Many cryptocurrency platforms are vulnerable to Maximal Extractable Value (MEV) attacks, where a malicious consensus leader can inject transactions or change the order of user transactions to maximize its profit. A promising line of…
Front-running attacks, which benefit from advanced knowledge of pending transactions, have proliferated in the blockchain space since the emergence of decentralized finance. Front-running causes devastating losses to honest participants and…
This paper presents C8s, a confidential computing architecture for Kubernetes that provides cryptographically rooted confidentiality, integrity, and verifiability guarantees for Kubernetes clusters from infrastructure operators. These…
The growing availability of hardware-based trusted execution environments (TEEs) in commodity processors has recently advanced support (i.e., design, implementation and deployment frameworks) for network-based secure services. Examples of…
Permission-less blockchains can realise trustless trust, albeit at the cost of limiting the complexity of computation tasks. To explain the implications for scalability, we have implemented a trust model for smart contracts, described as…
This paper presents LinBFT, a novel Byzantine fault tolerance (BFT) protocol for blockchain systems that achieves amortized O(n) communication volume per block under reasonable conditions (where n is the number of participants), while…
Facilitated by mobile edge computing, client-edge-cloud hierarchical federated learning (HFL) enables communication-efficient model training in a widespread area but also incurs additional security and privacy challenges from intermediate…
The main premise of federated learning (FL) is that machine learning model updates are computed locally to preserve user data privacy. This approach avoids by design user data to ever leave the perimeter of their device. Once the updates…
Cryptocurrencies employ auction-esque transaction fee mechanisms (TFMs) to allocate transactions to blocks, and to determine how much fees miners can collect from transactions. Several impossibility results show that TFMs that satisfy a…
This paper presents LinSBFT, a Byzantine Fault Tolerance (BFT) protocol with the capacity of processing over 2000 smart contract transactions per second in production. LinSBFT applies to a permissionless, public blockchain system, in which…
Distributed consensus protocols reach agreement among $n$ players in the presence of $f$ adversaries; different protocols support different values of $f$. Existing works study this problem for different adversary types (captured by threat…
Blockchains rely on economic incentives to ensure secure and decentralised operation, making incentive compatibility a core design concern. However, protocols are rarely deployed in isolation. Applications interact with the underlying…
Transaction Fee Mechanisms (TFMs) study auction design in the Blockchain context, and emphasize robustness against miner and user collusion, moreso than traditional auction theory. \cite{chung2023foundations} introduce the notion of a…
We initiate the study of transaction fee mechanism design for blockchain protocols in which multiple block producers contribute to the production of each block. Our contributions include: - We propose an extensive-form (multi-stage) game…
We introduce Zef, the first Byzantine-Fault Tolerant (BFT) protocol to support payments in anonymous digital coins at arbitrary scale. Zef follows the communication and security model of FastPay: both protocols are asynchronous,…
Federated learning (FL) enables collaborative model training across decentralized clients while preserving data privacy. However, its open-participation nature exposes it to data-poisoning attacks, in which malicious actors submit corrupted…
Ensuring that an AI system behaves reliably and as intended, especially in the presence of unexpected faults or adversarial conditions, is a complex challenge. Inspired by the field of Byzantine Fault Tolerance (BFT) from distributed…
Financial fraud cases are on the rise even with the current technological advancements. Due to the lack of inter-organization synergy and because of privacy concerns, authentic financial transaction data is rarely available. On the other…