Lucianna Kiffer, Sophia Skorik, Yann Vonlanthen, Roger Wattenhofer
On 15th September 2022, The Merge marked the Ethereum network's transition from computation-hardness-based consensus (proof-of-work) to a committee-based consensus mechanism (proof-of-stake). As a result, all the specialized hardware and GPUs that were being used by miners ceased to be profitable in the main Ethereum network. Miners were then left with the decision of how to re-purpose their hardware. One such choice was to try and make a profit mining another existing PoW system. In this study, we explore this choice by analyzing the hashrate increase in the top PoW networks following the merge. Our findings reveal that the peak increase in hashrate to other PoW networks following The Merge represents an adoption of at least 41% of the hashrate that was present in Ethereum, with 12% remaining more than 5 months later. Though we measure a drastic decrease in profitability by almost an order of magnitude, the continued presence of miners halts claims that power consumption was instantly addressed by Ethereum's switch to PoS.
In recent years, blockchain has been widely applied in the Internet of Things (IoT). Blockchain oracle, as a bridge for data communication between blockchain and off-chain, has also received significant attention. However, the numerous and heterogeneous devices in the IoT pose great challenges to the efficiency and security of data acquisition for oracles. We find that the matching relationship between data sources and oracle nodes greatly affects the efficiency and service quality of the entire oracle system. To address these issues, this paper proposes a distributed and efficient oracle solution tailored for the IoT, enabling fast acquisition of real-time off-chain data. Specifically, we first design a distributed oracle architecture that combines both Trusted Execution Environment (TEE) devices and ordinary devices to improve system scalability, considering the heterogeneity of IoT devices. Secondly, based on the trusted node information provided by TEE, we determine the matching relationship between nodes and data sources, assigning appropriate nodes for tasks to enhance system efficiency. Through simulation experiments, our proposed solution has been shown to effectively improve the efficiency and service quality of the system, reducing the average response time by approximately 9.92\% compared to conventional approaches.
Christian Berger, Sadok Ben Toumia, Hans P. Reiser
Recent Byzantine fault-tolerant (BFT) state machine replication (SMR) protocols increasingly focus on scalability to meet the requirements of distributed ledger technology (DLT). Validating the performance of scalable BFT protocol implementations requires careful evaluation. Our solution uses network simulations to forecast the performance of BFT protocols while experimentally scaling the environment. Our method seamlessly plug-and-plays existing BFT implementations into the simulation without requiring code modification or re-implementation, which is often time-consuming and error-prone. Furthermore, our approach is also significantly cheaper than experiments with real large-scale cloud deployments. In this paper, we first explain our simulation architecture, which enables scalable performance evaluations of BFT systems through high-performance network simulations. We validate the accuracy of these simulations for predicting the performance of BFT systems by comparing simulation results with measurements of real systems deployed on cloud infrastructures. We found that simulation results display a reasonable approximation at a larger system scale, because the network eventually becomes the dominating factor limiting system performance. In the second part of our paper, we use our simulation method to evaluate the performance of PBFT and BFT protocols from the "blockchain generation", such as HotStuff and Kauri, in large-scale and realistic wide-area network scenarios, as well as under induced faults.
Jon Michael Aanes, Jesper Balman Gravgaard, Peter Bro Miltersen, Kurt Nielsen · 5 authors
We consider Uniswap-like automated market makers, and, specifically, constant product liquidity pools, operating on blockchains. An important feature of Uniswap is the ability for a trader to carry out a sequence of asset swaps atomically, without other traders changing the prices along the way. This atomic-execution feature is not immediately available in cross-chain or sharded blockchain settings, where different liquidity pools are distributed across different chains or shards. Our contribution is a description and suggested implementation of a new functionality that might be added to individual liquidity pools, the {\em lock-swap}. The lock-swap enables a trader to get a guarantee for the price associated with a swap but only decide later whether or not to carry out the swap. Applied across several liquidity pools, it guarantees the trader assured prices for all swaps in a swap sequence and lets these prices inform the trader's decision about whether or not to carry out the sequence, thus essentially giving the trader the same benefits an atomic execution of the sequence would have provided him. However, in contrast to an atomic execution, our functionality does not prevent other traders from doing swaps during the time where the sequence is planned and possibly carried out. Nor does it prevent liquidity providers from adding or removing liquidity to and from the liquidity pool in that time period.
Ahmad J. Alkhodair, Saraju P. Mohanty, Elias Kougianos
The most essential component of every Distributed Ledger Technology (DLT) is the Consensus Algorithm (CA), which enables users to reach a consensus in a decentralized and distributed manner. Numerous CA exist, but their viability for particular applications varies, making their trade-offs a crucial factor to consider when implementing DLT in a specific field. This article provided a comprehensive analysis of the various consensus algorithms used in distributed ledger technologies (DLT) and blockchain networks. We cover an extensive array of thirty consensus algorithms. Eleven attributes including hardware requirements, pre-trust level, tolerance level, and more, were used to generate a series of comparison tables evaluating these consensus algorithms. In addition, we discuss DLT classifications, the categories of certain consensus algorithms, and provide examples of authentication-focused and data-storage-focused DLTs. In addition, we analyze the pros and cons of particular consensus algorithms, such as Nominated Proof of Stake (NPoS), Bonded Proof of Stake (BPoS), and Avalanche. In conclusion, we discuss the applicability of these consensus algorithms to various Cyber Physical System (CPS) use cases, including supply chain management, intelligent transportation systems, and smart healthcare.
Giorgos Tsimos, Anastasios Kichidis, Alberto Sonnino, Lefteris Kokoris-Kogias
The need for high throughput and censorship resistance in blockchain technology has led to research on DAG-based consensus. The Sui blockchain protocol uses a variant of the Bullshark consensus algorithm due to its lower latency, but this leader-based protocol causes performance issues when candidate leaders crash. In this paper, we explore the ideas pioneered by Carousel on providing Leader-Utilization and present HammerHead. Unlike Carousel, which is built with a chained and pipelined consensus protocol in mind, HammerHead does not need to worry about chain quality as it is directly provided by the DAG, but needs to make sure that even though validators might commit blocks in different views the safety and liveness is preserved. Our implementation of HammerHead shows a slight performance increase in a faultless setting, and a drastic 2x latency reduction and up to 40% throughput increase when suffering faults (100 validators, 33 faults).
The goal of grassroots cryptocurrencies is to provide a foundation with which local digital economies can emerge independently of each other and of global digital platforms and global cryptocurrencies; can form and grow without initial capital or external credit; can trade with each other; and can gradually merge into a global digital economy. Grassroots cryptocurrencies turn mutual trust into liquidity and thus could be a powerful means for 'banking the unbanked'. Grassroots cryptocurrencies have not been provided yet with a payment system, which is the goal of this paper. Here, we present Grassroots Flash, a payment system for grassroots cryptocurrencies that employs the blocklace -- a DAG-like counterpart of the blockchain data structure. We analyze its security (safety, liveness, and privacy) and efficiency, prove that it is indeed grassroots.
Selfish Mining is strategic rule-breaking to maximize rewards in proof-of-work protocols [3] and Markov Decision Processes (MDPs) are the preferred tool for finding optimal strategies in Bitcoin [4, 10] and similar linear chain protocols [12]. Protocols increasingly adopt non-sequential chain structures [11], for which MDP analysis is more involved [2]. To date, researchers have tailored specific attack spaces for each protocol [2, 4, 5, 7, 10, 12]. Assumptions differ, and validating and comparing results is difficult. To overcome this, we propose a generic attack space that supports a wide range of DAG protocols, including Ethereum, Fruitchains, and Parallel Proof-of-Work. Our approach is modular: we specify each protocol as one program, and then derive the Selfish Mining MDPs automatically.
The recent advancement of Blockchain technology consolidates its status as a viable alternative for various domains. However, evaluating the performance of blockchain applications can be challenging due to the underlying infrastructure's complexity and distributed nature. Therefore, a reliable modelling approach is needed to boost Blockchain-based applications' development and evaluation. While simulation-based solutions have been researched, machine learning (ML) model-based techniques are rarely discussed in conjunction with evaluating blockchain application performance. Our novel research makes use of two ML model-based methods. Firstly, we train a $k$ nearest neighbour ($k$NN) and support vector machine (SVM) to predict blockchain performance using predetermined configuration parameters. Secondly, we employ the salp swarm optimization (SO) ML model which enables the investigation of optimal blockchain configurations for achieving the required performance level. We use rough set theory to enhance SO, hereafter called ISO, which we demonstrate to prove achieving an accurate recommendation of optimal parameter configurations; despite uncertainty. Finally, statistical comparisons indicate that our models have a competitive edge. The $k$NN model outperforms SVM by 5\% and the ISO also demonstrates a reduction of 4\% inaccuracy deviation compared to regular SO.
Hyperledger Fabric is one of the most popular permissioned blockchain platforms. Although many existing works on the overall system performance of Hyperledger Fabric are available, a decomposition of each phase in Hyperledger Fabric remains to be explored. Admittedly, the overall system performance of Hyperledger Fabric might provide an end-user with satisfied performance information when invoking a transaction; however, it is far from informative when deploying a distributed system with specific performance goals, except for understanding each phase in Hyperledger Fabric. In this paper, we develop a measurement framework to characterize each phase's transaction and block data in Hyperledger Fabric based on the Fabric SDK Nodejs, where we thoroughly analyze and open source the implementation details of the measurement framework. We evaluate the performance of Hyperledger Fabric and have some interesting observations; 1. The number of CPU cores has a linear impact on the throughput of an endorsing peer. 2. The Raft-based ordering service shows good scalability with the number of ordering service nodes. 3. The communication latencies between the client and service in Hyperledger Fabric are significant. We then identify each phase's dominant latency in Hyperledger Fabric via primitive operation analysis and propose a stochastic computation model for performance analysis. We also use the alpha-beta communication model to analyze the corresponding communication latency. Finally, we validate the accuracy of the performance model on both local and cloud clusters. The experiment results and the performance model help guide the deployment of the Hyperledger Fabric service.
Seminal work of Eyal and Sirer (2014) establishes that a strategic Bitcoin miner may strictly profit by deviating from the intended Bitcoin protocol, using a strategy now termed *selfish mining*. More specifically, any miner with $>1/3$ of the total hashrate can earn bitcoin at a faster rate by selfish mining than by following the intended protocol (depending on network conditions, a lower fraction of hashrate may also suffice). One convincing critique of selfish mining in practice is that the presence of a selfish miner is *statistically detectable*: the pattern of orphaned blocks created by the presence of a selfish miner cannot be explained by natural network delays. Therefore, if an attacker chooses to selfish mine, users can detect this, and this may (significantly) negatively impact the value of BTC. So while the attacker may get slightly more bitcoin by selfish mining, these bitcoin may be worth significantly less USD. We develop a selfish mining variant that is provably *statistically undetectable*: the pattern of orphaned blocks is statistically identical to a world with only honest miners but higher network delay. Specifically, we consider a stylized model where honest miners with network delay produce orphaned blocks at each height independently with probability $β'$. We propose a selfish mining strategy that instead produces orphaned blocks at each height independently with probability $β> β'$. We further show that our strategy is strictly profitable for attackers with $38.2\% \ll 50\%$ of the total hashrate (and this holds for all natural orphan rates $β'$).
Dynamically available total-order broadcast (TOB) protocols tolerate fluctuating participation, e.g., as high as 99% of their participants going offline, which is especially useful in permissionless blockchain environments. However, dynamically available TOB protocols are synchronous protocols, and they lose their safety guarantees during periods of asynchrony. This is a major issue in practice. In this paper, we propose a simple but effective mechanism for tolerating bounded periods of asynchrony in dynamically available TOB protocols that ensure safety deterministically. We propose to trade off assumptions limiting the online/offline churn rate in exchange for tolerating bounded asynchronous periods through the use of a configurable message-expiration period. In practice, this allows picking a small synchrony bound $δ$, and therefore obtain a fast protocol in the common case, knowing that the protocol tolerates occasional periods of duration at most $π>δ$ during which the bound does not hold. We show how to apply this idea to a state-of-the-art protocol to make it tolerate bounded periods of asynchrony.
As a trusted middleware connecting the blockchain and the real world, the blockchain oracle can obtain trusted real-time price information for financial applications such as payment and settlement, and asset valuation on the blockchain. However, the current oracle schemes face the dilemma of security and service quality in the process of node selection, and the implicit interest relationship in financial applications leads to a significant conflict of interest between the task publisher and the executor, which reduces the participation enthusiasm of both parties and system security. Therefore, this paper proposes an anonymous node selection scheme that anonymously selects nodes with high reputations to participate in tasks to ensure the security and service quality of nodes. Then, this paper also details the interest requirements and behavioral motives of all parties in the payment settlement and asset valuation scenarios. Under the hypothesis of rational man, an incentive mechanism based on the Stackelberg game is proposed. It can achieve equilibrium under the pursuit of the revenue of task publishers and executors, thereby ensuring the revenue of all types of users and improving the enthusiasm for participation. Finally, we verify the security of the proposed scheme through security analysis. The experimental results show that the proposed scheme can reduce the variance of obtaining price data by about 55\% while ensuring security, and meeting the revenue of all parties.
Lately, the energy communities have gained a lot of attention as they have the potential to significantly contribute to the resilience and flexibility of the energy system, facilitating widespread integration of intermittent renewable energy sources. Within these communities the prosumers can engage in peer-to-peer trading, fostering local collaborations and increasing awareness about energy usage and flexible consumption. However, even under these favorable conditions, prosumer engagement levels remain low, requiring trading mechanisms that are aligned with their social values and expectations. In this paper, we introduce an innovative hedonic game coordination and cooperation model for P2P energy trading among prosumers which considers the social relationships within an energy community to create energy coalitions and facilitate energy transactions among them. We defined a heuristic that optimizes the prosumers coalitions, considering their social and energy price preferences and balancing the energy demand and supply within the community. We integrated the proposed hedonic game model into a state-of-the-art blockchain-based P2P energy flexibility market and evaluated its performance within an energy community of prosumers. The evaluation results on a blockchain-based P2P energy flexibility market show the effectiveness in considering social factors when creating coalitions, increasing the total amount of energy transacted in a market session by 5% compared with other game theory-based solutions. Finally, it shows the importance of the social dimensions of P2P energy transactions, the positive social dynamics in the energy community increasing the amount of energy transacted by more than 10% while contributing to a more balanced energy demand and supply within the community.
This paper addresses the issue of blockchain protocol risks, a foundational category of risks affecting Distributed Ledger Technology (DLT) which underpins digital assets, smart contracts, and decentralised applications. It presents a comprehensive risk management framework developed in collaboration with financial institutions, blockchain development teams and regulators that applies a traditional risk management taxonomy to address certain overlooked blockchain protocol risks. The approach offers a structured way to identify, measure, monitor and report blockchain protocol risks. The paper provides real-world use cases to demonstrate the practicality and implementation of the proposed framework. The findings of this work contribute to the evolving understanding of blockchain protocol risks and provide valuable insights on how these risks affect the adoption of DLT by financial institutions.
Mohammad M. Jalalzai, Alexander Mozeika, Marcin P. Pawlowski, Ganesh Narayanaswamy
We present Carnot, a leader-based Byzantine Fault Tolerant (BFT) consensus protocol that is responsive and operates under the partially synchronous model. Responsive BFT consensus protocols exhibit wire-speed operation and deliver instantaneous finality, thereby addressing a fundamental need in distributed systems. A key challenge in scaling these protocols has been the computational complexity associated with authenticator verification. We demonstrate that Carnot effectively addresses this bottleneck by adeptly streamlining the verification and aggregation of $O(log(N))$ authenticators per node. This notable advancement marks a substantial improvement over the prevailing $O(N)$ state-of-the-art approaches. Leveraging this inherent property, Carnot demonstrates its capacity to seamlessly scale to networks comprising tens to hundreds of thousands of nodes. We envision Carnot as a critical stride towards bridging the gap between classical BFT consensus mechanisms and blockchain technology.
Tran Viet Khoa, Do Hai Son, Chi-Hieu Nguyen, Dinh Thai Hoang · 11 authors
With the escalating prevalence of malicious activities exploiting vulnerabilities in blockchain systems, there is an urgent requirement for robust attack detection mechanisms. To address this challenge, this paper presents a novel collaborative learning framework designed to detect attacks in blockchain transactions and smart contracts by analyzing transaction features. Our framework exhibits the capability to classify various types of blockchain attacks, including intricate attacks at the machine code level (e.g., injecting malicious codes to withdraw coins from users unlawfully), which typically necessitate significant time and security expertise to detect. To achieve that, the proposed framework incorporates a unique tool that transforms transaction features into visual representations, facilitating efficient analysis and classification of low-level machine codes. Furthermore, we propose an advanced collaborative learning model to enable real-time detection of diverse attack types at distributed mining nodes. Our model can efficiently detect attacks in smart contracts and transactions for blockchain systems without the need to gather all data from mining nodes into a centralized server. In order to evaluate the performance of our proposed framework, we deploy a pilot system based on a private Ethereum network and conduct multiple attack scenarios to generate a novel dataset. To the best of our knowledge, our dataset is the most comprehensive and diverse collection of transactions and smart contracts synthesized in a laboratory for cyberattack detection in blockchain systems. Our framework achieves a detection accuracy of approximately 94% through extensive simulations and 91% in real-time experiments with a throughput of over 2,150 transactions per second.
We study an adversary who attacks a Proof-of-Work (POW) blockchain by selfishly constructing an alternative longest chain. We characterize optimal strategies employed by the adversary when a difficulty adjustment rule alà Bitcoin applies. As time (namely the times-tamp specified in each block) in most permissionless POW blockchains is somewhat subjective, we focus on two extreme scenarios: when time is completely verifiable, and when it is completely unverifiable. We conclude that an adversary who faces a difficulty adjustment rule will find a longest-chain attack very challenging when timestamps are verifiable. POW blockchains with frequent difficulty adjustments relative to time reporting flexibility will be substantially more vulnerable to longest-chain attacks. Our main fining provides guidance on the design of difficulty adjustment rules and demonstrates the importance of timestamp verifiability.
Blockchain has become a popular decentralized paradigm for various applications in the zero-trust environment. The core of the blockchain is the consensus protocol, which establishes consensus among all the participants. PoW (Proof-of-Work) is one of the most popular consensus protocols. However, the PoW consensus protocol which incentives the participants to use their computing power to solve a meaningless hash puzzle is continuously questioned as energy-wasting. To address these issues, we propose an efficient and secure consensus protocol based on proof of useful federated learning for blockchain (called FedChain). We first propose a secure and robust blockchain architecture that takes federated learning tasks as proof of work. Then a pool aggregation mechanism is integrated to improve the efficiency of the FedChain architecture. To protect model parameter privacy for each participant within a mining pool, a secret sharing-based ring-all reduce architecture is designed. We also introduce a data distribution-based federated learning model optimization algorithm to improve the model performance of FedChain. At last, a zero-knowledge proof-based federated learning model verification is introduced to preserve the privacy of federated learning participants while proving the model performance of federated learning participants. Our approach has been tested and validated through extensive experiments, demonstrating its performance.
Federated learning (FL) aims to collaboratively train a global model while ensuring client data privacy. However, FL faces challenges from the non-IID data distribution among clients. Clustered FL (CFL) has emerged as a promising solution, but most existing CFL frameworks adopt synchronous frameworks lacking asynchrony. An asynchronous CFL framework called SDAGFL based on directed acyclic graph distributed ledger techniques (DAG-DLT) was proposed, but its complete decentralization leads to high communication and storage costs. We propose DAG-ACFL, an asynchronous clustered FL framework based on directed acyclic graph distributed ledger techniques (DAG-DLT). We first detail the components of DAG-ACFL. A tip selection algorithm based on the cosine similarity of model parameters is then designed to aggregate models from clients with similar distributions. An adaptive tip selection algorithm leveraging change-point detection dynamically determines the number of selected tips. We evaluate the clustering and training performance of DAG-ACFL on multiple datasets and analyze its communication and storage costs. Experiments show the superiority of DAG-ACFL in asynchronous clustered FL. By combining DAG-DLT with clustered FL, DAG-ACFL realizes robust, decentralized and private model training with efficient performance.
Ittai Abraham, Danny Dolev, Ittay Eyal, Joseph Y. Halpern
We present Colordag, a blockchain protocol where following the prescribed strategy is, with high probability, a best response as long as all miners have less than 1/2 of the mining power. We prove the correctness of Colordag even if there is an extremely powerful adversary who knows future actions of the scheduler: specifically, when agents will generate blocks and when messages will arrive. The state-of-the-art protocol, Fruitchain, is an epsilon-Nash equilibrium as long as all miners have less than 1/2 of the mining power. However, there is a simple deviation that guarantees that deviators are never worse off than they would be by following Fruitchain, and can sometimes do better. Thus, agents are motivated to deviate. Colordag implements a solution concept that we call epsilon-sure Nash equilibrium and does not suffer from this problem. Because it is an epsilon-sure Nash equilibrium, Colordag is an epsilon Nash equilibrium and with probability (1 - epsilon) is a best response.
Mark C. Ballandies, Hongyang Wang, Andrew Chung Chee Law, Joshua C. Yang · 6 authors
As digitalization and technological advancements continue to shape the infrastructure landscape, the emergence of blockchain-based decentralized physical infrastructure networks (DePINs) has gained prominence. However, a systematic categorization of DePIN components and their interrelationships is still missing. To address this gap, we conduct a literature review and analysis of existing frameworks and derived a taxonomy of DePIN systems from a conceptual architecture. Our taxonomy encompasses three key dimensions: distributed ledger technology, cryptoeconomic design and physicial infrastructure network. Within each dimension, we identify and define relevant components and attributes, establishing a clear hierarchical structure. Moreover, we illustrate the relationships and dependencies among the identified components, highlighting the interplay between governance models, hardware architectures, networking protocols, token mechanisms, and distributed ledger technologies. This taxonomy provides a foundation for understanding and classifying diverse DePIN networks, serving as a basis for future research and facilitating knowledge exchange, fostering collaboration and standardization within the emerging field of decentralized physical infrastructure networks.
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 model aggregations and remains the single point of failure issue. To tackle these challenges, we propose a blockchain-based HFL (BHFL) system that operates a permissioned blockchain among edge servers for model aggregation without the need for a centralized cloud server. The employment of blockchain, however, introduces additional overhead. To enable a compact and efficient workflow, we design a novel lightweight consensus algorithm, named Proof of Federated Edge Learning (PoFEL), to recycle the energy consumed for local model training. Specifically, the leader node is selected by evaluating the intermediate FEL models from all edge servers instead of other energy-wasting but meaningless calculations. This design thus improves the system efficiency compared with traditional BHFL frameworks. To prevent model plagiarism and bribery voting during the consensus process, we propose Hash-based Commitment and Digital Signature (HCDS) and Bayesian Truth Serum-based Voting (BTSV) schemes. Finally, we devise an incentive mechanism to motivate continuous contributions from clients to the learning task. Experimental results demonstrate that our proposed BHFL system with the corresponding consensus protocol and incentive mechanism achieves effectiveness, low computational cost, and fairness.
Traditionally, peer-to-peer systems have relied on altruism and reciprocity. Although incentive-based models have gained prominence in new-generation peer-to-peer systems, it is essential to recognize the continued importance of cooperative principles in achieving performance, fairness, and correctness. The lack of this acknowledgment has paved the way for selfish peers to gain unfair advantages in these systems. As such, we address the challenge of selfish peers by devising a mechanism to reward sustained cooperation. Instead of relying on global accountability mechanisms, we propose a protocol that naturally aggregates local evaluations of cooperation. Traditional mechanisms are often vulnerable to Sybil and misreporting attacks. However, our approach overcomes these issues by limiting the benefits selfish peers can gain without incurring any cost. The viability of our algorithm is proven with a deployment to 27,259 Internet users and a realistic simulation of a blockchain gossip protocol. We show that our protocol sustains cooperation even in the presence of a majority of selfish peers while incurring only negligible overhead.