This paper presents the results of a comprehensive empirical study of losses to arbitrageurs (following the formalization of loss-versus-rebalancing by [Milionis et al., 2022]) incurred by liquidity providers on automated market makers (AMMs). We show that those losses exceed the fees earned by liquidity providers across many of the largest AMM liquidity pools (on Uniswap). Remarkably, we also find that the Uniswap v2 pools are more profitable for passive LPs than their Uniswap v3 counterparts. We also investigate how arbitrage losses change with block times. As expected, arbitrage losses decrease when block production is faster. However, the rate of the decline varies significantly across different trading pairs. For instance, when comparing 100ms block times to Ethereum's current 12-second block times, the decrease in losses to arbitrageurs ranges between 20% to 70%, depending on the specific trading pair.
Elitsa Pankovska, Ashish Rajendra Sai, Harald Vranken, Alan Ransil
The high electricity consumption of cryptocurrencies that rely on proof-of-work (PoW) consensus algorithms has raised serious environmental concerns due to its association with carbon emissions and strain on energy grids. There has been significant research into estimating the electricity consumption of PoW-based cryptocurrencies and developing alternatives to PoW. In this article, we introduce refined models to estimate the electricity consumption of two prominent alternatives: Ethereum, now utilizing proof-of-stake (PoS), and Filecoin, which employs proof-of-spacetime (PoSt). Ethereum stands as a leading blockchain platform for crafting decentralized applications, whereas Filecoin is recognized as the world's foremost decentralized data storage network. Prior studies for modeling electricity consumption have been criticized for methodological flaws and shortcomings, low-quality data, and unvalidated assumptions. We improve on this in several ways: we obtain more novel, validated data from the systems in question, extract information from existing data and research, and we improve transparency and reproducibility by clearly explaining and documenting the used methodology and explicitly stating unavoidable limitations and assumptions made. When comparing the current, most prominent models for Ethereum and Filecoin to our refined models, we find that given the wide error margins of both the refined models and the ones introduced in prior literature, the resulting average estimates are to a large extent in line with each other.
Arian Baloochestani, Hanish Gogada, Leander Jehl, Hein Meling
Many blockchain platforms use committee-based consensus for scalability, finality, and security. In this consensus scheme, a committee decides which blocks get appended to the chain, typically through several voting phases. Platforms typically leverage the committee members' recorded votes to reward, punish, or detect failures. A common approach is to let the block proposer decide which votes to include, opening the door to possible attacks. For example, a malicious proposer can omit votes from targeted committee members, resulting in lost profits and, ultimately, their departure from the system. This paper presents Iniva, an inclusive and incentive-compatible vote aggregation scheme that prevents such vote omission attacks. Iniva relies on a tree overlay with carefully selected fallback paths, making it robust against process failures without needing reconfiguration or additional redundancy. Our analysis shows that Iniva significantly reduces the chance to omit individual votes while ensuring that omitting many votes incurs a significant cost. In addition, our experimental results show that Iniva enjoys robustness, scalability, and reasonable throughput.
Ramesh Adhikari, Costas Busch, Dariusz R. Kowalski
Sharding is used to improve the scalability and performance of blockchain systems. We investigate the stability of blockchain sharding, where transactions are continuously generated by an adversarial model. The system consists of $n$ processing nodes that are divided into $s$ shards. Following the paradigm of classical adversarial queuing theory, transactions are continuously received at injection rate $ρ\leq 1$ and burstiness $b > 0$. We give an absolute upper bound $\max\{ \frac{2}{k+1}, \frac{2}{ \left\lfloor\sqrt{2s}\right\rfloor}\}$ on the maximum injection rate for which any scheduler could guarantee bounded queues and latency of transactions, where $k$ is the number of shards that each transaction accesses. We next give a basic distributed scheduling algorithm for uniform systems where shards are equally close to each other. To guarantee stability, the injection rate is limited to $ρ\leq \max\{ \frac{1}{18k}, \frac{1}{\lceil 18 \sqrt{s} \rceil} \}$. We then provide a fully distributed scheduling algorithm for non-uniform systems where shards are arbitrarily far from each other. By using a hierarchical clustering of the shards, stability is guaranteed with injection rate $ρ\leq \frac{1}{c_1d \log^2 s} \cdot \max\{ \frac{1}{k}, \frac{1}{\sqrt{s}} \}$, where $d$ is the worst distance of any transaction to the shards it will access, and $c_1$ is some positive constant. We also conduct simulations to evaluate the algorithms and measure the average queue sizes and latency throughout the system. To our knowledge, this is the first adversarial stability analysis of sharded blockchain systems.
This article examines the significant challenges encountered in implementing sharding within distributed replication systems. It identifies the impediments of achieving consensus among large participant sets, leading to scalability, throughput, and performance limitations. These issues primarily arise due to the message complexity inherent in consensus mechanisms. In response, we investigate the potential of sharding to mitigate these challenges, analyzing current implementations within distributed replication systems. Additionally, we offer a comprehensive review of replication systems, encompassing both classical distributed databases as well as Distributed Ledger Technologies (DLTs) employing sharding techniques. Through this analysis, the article aims to provide insights into addressing the scalability and performance concerns in distributed replication systems.
The blockchain ecosystem, particularly with the rise of Web3 and Non-Fungible Tokens (NFTs), has experienced a significant increase in users and applications. However, this expansion is challenged by the need to connect early adopters with a wider user base. A notable difficulty in this process is the complex interfaces of blockchain wallets, which can be daunting for those familiar with traditional payment methods. To address this issue, the category of "embedded wallets" has emerged as a promising solution. These wallets are seamlessly integrated into the front-end of decentralized applications (Dapps), simplifying the onboarding process for users and making access more widely available. However, our insights indicate that this simplification introduces a trade-off between ease of use and security. Embedded wallets lack transparency and auditability, leading to obscured transactions by the front end and a pronounced risk of fraud and phishing attacks. This paper proposes a new protocol to enhance the security of embedded wallets. Our VELLET protocol introduces a wallet verifier that can match the audit trail of embedded wallets on smart contracts, incorporating a process to verify authenticity and integrity. In the implementation architecture of the VELLET protocol, we suggest using the Text Record feature of the Ethereum Name Service (ENS), known as a decentralized domain name service, to serve as a repository for managing the audit trails of smart contracts. This approach has been demonstrated to reduce the necessity for new smart contract development and operational costs, proving cost-effective through a proof-of-concept. This protocol is a vital step in reducing security risks associated with embedded wallets, ensuring their convenience does not undermine user security and trust.
Groundhog is a novel design for a smart contract execution engine based around concurrent execution of blocks of transactions. Unlike prior work, transactions within a block in Groundhog are not ordered relative to one another. Instead, our key design insights are first, to design a set of commutative semantics that lets the Groundhog runtime deterministically resolve concurrent accesses to shared data. Second, some storage accesses (such as withdrawing money from an account) conflict irresolvably; Groundhog therefore enforces validity constraints on persistent storage accesses via a reserve-commit process. These two ideas give Groundhog a set of semantics that, while not as powerful as traditional sequential semantics, are flexible enough to implement a wide variety of important applications, and are strictly more powerful than the semantics used in some production blockchains today. Unlike prior smart contract systems, transactions throughput never suffers from contention between transactions. Using 96 CPU cores, Groundhog can process more than half a million payment transactions per second, whether between 10M accounts or just 2.
Although the decentralized storage technology based on the blockchain can effectively realize secure data storage on cloud services. However, there are still some problems in the existing schemes, such as low storage capacity and low efficiency. To address related issues, we propose a novel decentralized storage framework, which mainly includes four aspects: (1) we proposed a Bi-direction Circular Linked Chain Structure (BCLCS), which improves data's storage capacity and applicability in decentralized storage. (2) A Proof of Resources (PoR) decision model is proposed. By introducing the network environment as an essential evaluation parameter of storage right decision, the energy and time consumption of decision-making are reduced, and the fairness of decision-making is improved. (3) A chain structure dynamic locking mechanism (CSDLM) is designed to realize anti-traverse and access control. (4) A Bi-directional data Access Mechanism (BDAM) is proposed, which improves the efficiency of data access and acquisition in decentralized storage mode. The experimental results show that the framework has significantly improved the shortcomings of the current decentralized storage.
Development of blockchain smart contracts is more difficult than mainstream software development because the underlying blockchain infrastructure poses additional complexity. To ease the developer's task of writing smart contract, as other research efforts, we also use Business Process Model and Notation BPMN modeling to describe application requirements for trade of goods and services and then transform automatically the BPMN model into the methods of a smart contract. In our previous research we described our approach and a tool to Transform Automatically BPMN models into Smart contracts TABS. In this paper, we describe how the TABS approach is augmented with the support for a BPMN collaborative transaction by several actors. Our approach analyzes the BPMN model to determine which patterns in the BPMN model are suitable for use as collaborative transactions. The found BPMN patterns that are suitable as transactions are shown to the developer who decides which ones should be deployed as collaborative transactions. We describe how our approach automatically transform the BPMN model into smart contract the provides a transaction mechanism to enforce the transactional properties of the nested transactions. Our approach greatly reduces the developers task as synchronization of collaborative activities is provided by our approach, so that the developer needs to code only independent tasks with well-defined inputs and outputs. We also overview the TABS+ tool we built as a proof of concept to show that our approach is feasible. Finally, we provide estimates on the cost of supporting the nested BPMN collaborative transactions.
This paper presents two methodological advancements in decentralized multi-task learning under privacy constraints, aiming to pave the way for future developments in next-generation Blockchain platforms. First, we expand the existing framework for collaborative dictionary learning (CollabDict), which has previously been limited to Gaussian mixture models, by incorporating deep variational autoencoders (VAEs) into the framework, with a particular focus on anomaly detection. We demonstrate that the VAE-based anomaly score function shares the same mathematical structure as the non-deep model, and provide comprehensive qualitative comparison. Second, considering the widespread use of "pre-trained models," we provide a mathematical analysis on data privacy leakage when models trained with CollabDict are shared externally. We show that the CollabDict approach, when applied to Gaussian mixtures, adheres to a Renyi differential privacy criterion. Additionally, we propose a practical metric for monitoring internal privacy breaches during the learning process.
The metaverse, envisioned as the next digital frontier for avatar-based virtual interaction, involves high-performance models. In this dynamic environment, users' tasks frequently shift, requiring fast model personalization despite limited data. This evolution consumes extensive resources and requires vast data volumes. To address this, meta-learning emerges as an invaluable tool for metaverse users, with federated meta-learning (FML), offering even more tailored solutions owing to its adaptive capabilities. However, the metaverse is characterized by users heterogeneity with diverse data structures, varied tasks, and uneven sample sizes, potentially undermining global training outcomes due to statistical difference. Given this, an urgent need arises for smart coalition formation that accounts for these disparities. This paper introduces a dual game-theoretic framework for metaverse services involving meta-learners as workers to manage FML. A blockchain-based cooperative coalition formation game is crafted, grounded on a reputation metric, user similarity, and incentives. We also introduce a novel reputation system based on users' historical contributions and potential contributions to present tasks, leveraging correlations between past and new tasks. Finally, a Stackelberg game-based incentive mechanism is presented to attract reliable workers to participate in meta-learning, minimizing users' energy costs, increasing payoffs, boosting FML efficacy, and improving metaverse utility. Results show that our dual game framework outperforms best-effort, random, and non-uniform clustering schemes - improving training performance by up to 10%, cutting completion times by as much as 30%, enhancing metaverse utility by more than 25%, and offering up to 5% boost in training efficiency over non-blockchain systems, effectively countering misbehaving users.
While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Merging distributed computing with cryptographic techniques, decentralized technologies introduce a novel computing paradigm. Blockchain ensures secure, transparent, and tamper-proof data management by validating and recording transactions via consensus across network nodes. Federated Learning (FL), as a distributed machine learning framework, enables participants to collaboratively train models while safeguarding data privacy by avoiding direct raw data exchange. Despite the growing interest in decentralized methods, their application in FL remains underexplored. This paper presents a thorough investigation into Blockchain-based FL (BCFL), spotlighting the synergy between blockchain's security features and FL's privacy-preserving model training capabilities. First, we present the taxonomy of BCFL from three aspects, including decentralized, separate networks, and reputation-based architectures. Then, we summarize the general architecture of BCFL systems, providing a comprehensive perspective on FL architectures informed by blockchain. Afterward, we analyze the application of BCFL in healthcare, IoT, and other privacy-sensitive areas. Finally, we identify future research directions of BCFL.
Byzantine consensus protocols are essential in blockchain technology. The widely recognized HotStuff protocol uses cryptographic measures for efficient view changes and reduced communication complexity. Recently, the main authors of HotStuff introduced an advanced iteration named HotStuff-2. This paper aims to compare the principles and analyze the effectiveness of both protocols, hoping to depict their key differences and assess the potential enhancements offered by HotStuff-2.
Zaynah Dargaye, Önder Gürcan, Florent Kirchner, Sara Tucci-Piergiovanni
Distributed immutable ledgers, or blockchains, allow the secure digitization of evidential transactions without relying on a trusted third-party. Evidential transactions involve the exchange of any form of physical evidence, such as money, birth certificate, visas, tickets, etc. Most of the time, evidential transactions occur in the context of complex procedures, called evidential protocols, among physical agents. The blockchain provides the mechanisms to transfer evidence, while smart contracts - programs executing within the blockchain in a decentralized and replicated fashion - allow encoding evidential protocols on top of a blockchain. As a smart contract foregoes trusted third-parties and runs on several machines anonymously, it constitutes a highly critical program that has to be secure and trusted-by-design. While most of the current smart contract languages focus on easy programmability, they do not directly address the need of guaranteeing trust and accountability, which becomes a significant issue when evidential protocols are encoded as smart contracts.
Gabriele Morello, Mojtaba Eshghie, Sofia Bobadilla, Martin Monperrus
The DISL dataset features a collection of $514,506$ unique Solidity files that have been deployed to Ethereum mainnet. It caters to the need for a large and diverse dataset of real-world smart contracts. DISL serves as a resource for developing machine learning systems and for benchmarking software engineering tools designed for smart contracts. By aggregating every verified smart contract from Etherscan up to January 15, 2024, DISL surpasses existing datasets in size and recency.
Blockchain technology has emerged as a necessity for the decentralization of payment methods and transactions, but it has brought with it many properties of distributed systems that have made it a crucial technology for overcoming some of society's challenges, especially in the context of decentralizing services, transparency of information, availability, and security. Its architecture and communication methods, although possessing some complex nuances to understand, particularly for the lay audience in the field of distributed systems, protocols, and computer networks. In this article, we will explore some topics of distributed systems related to blockchain technology.
Erwan Mahe, Rouwaida Abdallah, Sara Tucci-Piergiovanni, Pierre-Yves Piriou
This paper presents a novel adversary model specifically tailored to distributed systems, aiming to assess the security of blockchain networks. Building upon concepts such as adversarial assumptions, goals, and capabilities, our proposed adversary model classifies and constrains the use of adversarial actions based on classical distributed system models, defined by both failure and communication models. The objective is to study the effects of these allowed actions on the properties of distributed protocols under various system models. A significant aspect of our research involves integrating this adversary model into the Multi-Agent eXperimenter (MAX) framework. This integration enables fine-grained simulations of adversarial attacks on blockchain networks. In this paper, we particularly study four distinct fairness properties on Hyperledger Fabric with the Byzantine Fault Tolerant Tendermint consensus algorithm being selected for its ordering service. We define novel attacks that combine adversarial actions on both protocols, with the aim of violating a specific client-fairness property. Simulations confirm our ability to violate this property and allow us to evaluate the impact of these attacks on several order-fairness properties that relate orders of transaction reception and delivery.
Ethereum's current Gasper consensus mechanism, which combines the Latest Message Driven Greediest Heaviest Observed SubTree (LMD-GHOST) fork choice rule with the probabilistic Casper the Friendly Finality Gadget (FFG) finality overlay, finalizes transactions in 64 to 95 blocks, an approximate 15-minute delay. This finalization latency impacts user experience and exposes the network to short-term chain reorganization risks, potentially enabling transaction censorship or frontrunning by validators without severe penalties. As the ecosystem pursues a rollup-centric roadmap to scale Ethereum into a secure global settlement layer, faster finality allows cross-layer and inter-rollup communication with greater immediacy, reducing capital inefficiencies. Single slot finality (SSF), wherein transactions are finalized within the same slot they are proposed, promises to advance the Ethereum protocol and enable better user experiences by enabling near-instant economic finality. This thesis systematically studies distributed consensus protocols through propose-vote-merge, PBFT-inspired, and graded agreement families - scrutinizing their capacities to enhance or replace LMD-GHOST. The analysis delves into the intricate tradeoffs between safety, liveness, and finality, shedding light on the challenges and opportunities in designing an optimal consensus protocol for Ethereum. It also explores different design decisions and mechanisms by which single slot or fast finality can be enabled, including cumulative finality, subsampling, and application-layer fast finality. Furthermore, this work introduces SSF-enabled and streamlined fast finality constructions based on a single-vote total order broadcast protocol. The insights and recommendations in this thesis provide a solid foundation for the Ethereum community to make informed decisions regarding the future direction of the protocol's consensus.
Tri Nguyen, Huong Nguyen, Ahmad Ijaz, Saeid Sheikhi · 6 authors
The rapid integration of Generative AI (GenAI) and Large Language Models (LLMs) in sectors such as education and healthcare have marked a significant advancement in technology. However, this growth has also led to a largely unexplored aspect: their security vulnerabilities. As the ecosystem that includes both offline and online models, various tools, browser plugins, and third-party applications continues to expand, it significantly widens the attack surface, thereby escalating the potential for security breaches. These expansions in the 6G and beyond landscape provide new avenues for adversaries to manipulate LLMs for malicious purposes. We focus on the security aspects of LLMs from the viewpoint of potential adversaries. We aim to dissect their objectives and methodologies, providing an in-depth analysis of known security weaknesses. This will include the development of a comprehensive threat taxonomy, categorizing various adversary behaviors. Also, our research will concentrate on how LLMs can be integrated into cybersecurity efforts by defense teams, also known as blue teams. We will explore the potential synergy between LLMs and blockchain technology, and how this combination could lead to the development of next-generation, fully autonomous security solutions. This approach aims to establish a unified cybersecurity strategy across the entire computing continuum, enhancing overall digital security infrastructure.
Pierre Civit, Muhammad Ayaz Dzulfikar, Seth Gilbert, Rachid Guerraoui · 7 authors
Byzantine agreement enables n processes to agree on a common L-bit value, despite up to t > 0 arbitrary failures. A long line of work has been dedicated to improving the bit complexity of Byzantine agreement in synchrony. This has culminated in COOL, an error-free (deterministically secure against a computationally unbounded adversary) solution that achieves O(nL + n^2 logn) worst-case bit complexity (which is optimal for L >= n logn according to the Dolev-Reischuk lower bound). COOL satisfies strong unanimity: if all correct processes propose the same value, only that value can be decided. Strong unanimity is, however, not sufficient for today's state machine replication (SMR) and blockchain protocols. These systems value progress and require a decided value to always be valid, excluding default decisions (such as EMPTY) even in cases where there is no unanimity a priori. Validated Byzantine agreement satisfies this property (called external validity). Yet, the best error-free (or even signature-free) validated agreement solutions achieve only O(n^2L) bit complexity, a far cry from the Omega(nL + n^2) Dolev-Reishcuk lower bound. In this paper, we present two new synchronous algorithms for validated Byzantine agreement, HashExt and ErrorFreeExt, with different trade-offs. Both algorithms are (1) signature-free, (2) optimally resilient (tolerate up to t = n^2 kappa (where kappa is the size of a hash). On the other hand, ErrorFreeExt is error-free, using no cryptography whatsoever, and achieves O( (nL + n^2) logn ) bit complexity, which is near-optimal for any L.
A blockchain facilitates secure and atomic transactions between mutually untrusting parties on that chain. Today, there are multiple blockchains with differing interfaces and security properties. Programming in this multi-blockchain world is hindered by the lack of general and convenient abstractions for cross-chain communication and computation. Current cross-chain communication bridges have varied and low-level interfaces, making it difficult to develop portable applications. Current methods for multi-chain atomic transactions are limited in scope to cryptocurrency swaps. This work addresses these issues. We first define a uniform, high-level interface for communication between chains. Building on this interface, we formulate a protocol that guarantees atomicity for general transactions whose operations may span several chains. We formulate and prove the desired correctness and security properties of these protocols. Our prototype implementation is built using the LayerZero cross-chain bridge. Experience with this implementation shows that the new abstractions considerably simplify the design and implementation of multi-chain transactions. Experimental evaluation with multi-chain swap transactions demonstrates performance comparable to that of custom-built implementations.
Mehrzad Mohammadi, Reza Javan, Mohammad Beheshti-Atashgah, Mohammad Reza Aref
Internet of Things (IoT) devices are capable of allowing for far-reaching access to and evaluation of patient data to monitor health and diagnose from a distance. An electronic healthcare system that checks patient data, prepares medicines and provides financial assistance is necessary. Providing safe data transmission, monitoring, decentralization, preserving patient privacy, and maintaining confidentiality are essential to an electronic healthcare system. In this study, we introduce (SCALHEALTH) which is a blockchain-based scheme of the Hyperledger Fabric consortium. In this study, we use authentication to agree on a common key for data encryption to send data confidentially. Also, sending data through IPFS is decentralized. Non-fungible token (NFT) is used to send patient prescriptions to pharmacies and insurance companies to ensure the authenticity of patient prescriptions. As the system's main body, blockchain creates authorization and validation for all devices and institutions. Also, all metadata in the system is recorded on the blockchain to maintain integrity, transparency, and timely data monitoring. The proposed study uses two types of blockchain: a health blockchain and a financial blockchain. The financial blockchain is for financial transactions and is based on Ethereum. The health blockchain also introduces a mechanism that allows several blockchains to be active in parallel, instead of only one blockchain. The prototype of this mechanism is simulated in two scenarios. In comparison to the normal state, the proposed plan has superior results.
With the rapid growth of Internet of Things (IoT) applications, there's a big demand for more processing power and resources in devices. Mobile Edge Computing (MEC) looks promising for enhancing performance and reducing costs by offloading the computing work of IoT to MEC servers. However, the current methods for offloading have issues with privacy and security during the transfer of data and programs. To tackle this, recent developments have introduced secure offloading methods using Blockchain technology, which helps to make MEC more secure by building trust between nodes, improving how edges are authenticated and accessed, and stopping unauthorized access to devices. This paper reviews these Blockchain-based offloading methods for different MEC settings. It starts by explaining the key ideas in offloading and Blockchain, then it sorts the Blockchain-based offloading methods by the algorithms they use. It also compares the offloading methods in each group and ends with a discussion and comparison of the different techniques, tools, and metrics used in these methods.
This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks. We propose a simplified consensus-based verification process integrated with an adaptive thresholding mechanism. This dynamic thresholding is designed to adjust based on the evolving landscape of model updates, offering a refined layer of anomaly detection that aligns with the real-time needs of distributed learning environments. Our method necessitates a majority consensus among participating clients to validate updates, ensuring that only vetted and consensual modifications are applied to the global model. The efficacy of our approach is validated through experiments on two benchmark datasets in deep learning, CIFAR-10 and MNIST. Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience. This method transcends conventional techniques that depend on anomaly detection or statistical validation by incorporating a verification layer reminiscent of blockchain's participatory validation without the associated cryptographic overhead. The innovation of our approach rests in striking an optimal balance between heightened security measures and the inherent limitations of FL systems, such as computational efficiency and data privacy. Implementing a consensus mechanism specifically tailored for FL environments paves the way for more secure, robust, and trustworthy distributed machine learning applications, where safeguarding data integrity and model robustness is critical.