Aishwarya Parab, P. Pradhan, Yogesh Simmhan, Arnab K. Paul
The increasing availability of data from diverse sources, including trusted entities such as governments, as well as untrusted crowd-sourced contributors, demands a secure and trustworthy environment for storage and retrieval. Blockchain, as a distributed and immutable ledger, offers a promising solution to address these challenges. This short paper studies the feasibility of a blockchain-based framework for secure data storage and retrieval across trusted and untrusted sources, focusing on provenance, storage mechanisms, and smart contract security. Through initial experiments using Hyper Ledger Fabric (HLF), we evaluate the storage efficiency, scalability, and feasibility of the proposed approach. This study serves as a motivation for future research to develop a comprehensive blockchain-based storage and retrieval framework.
Blockchain consensus mechanisms must balance security, decentralization, and efficiency while ensuring fair participation. Proof of Team Sprint (PoTS) is a cooperative consensus mechanism designed to address the energy inefficiencies and centralization tendencies of traditional Proof of Work (PoW). Unlike PoW, where rewards disproportionately favor high-performance nodes, PoTS encourages collaboration by forming teams and distributing rewards more equitably among participants. In this study, we evaluate the fairness properties of PoTS by analyzing reward distribution under varying computational power distributions. Through extensive simulations, we compare equal-share allocation and proportional reward allocation, highlighting their impact on decentralization and participation. Our results demonstrate that PoTS significantly reduces reward disparity between high-performance and low-performance nodes, fostering a more inclusive ecosystem. Additionally, we observe that as team sizes increase, the influence of individual computational power is mitigated, allowing lower-performance nodes to contribute meaningfully. Moreover, our findings reveal that the marginal benefit of investing in extremely high-performance hardware diminishes, which discourages centralization and aligns incentives toward sustainable participation. We also discuss the economic implications of PoTS, particularly its potential to reshape blockchain mining strategies by balancing fairness with computational efficiency. These insights contribute to the broader discussion on blockchain fairness and provide a foundation for further research into cooperative consensus mechanisms.
This paper presents an empirical evaluation of the Proof of Team Sprint (PoTS) consensus algorithm, focusing on reward fairness, energy efficiency, system stability, and scalability. We conducted large-scale simulations comparing PoTS with conventional Proof of Work (PoW) across various team sizes and computational conditions. In PoW, the highest-performance node ranked first in all 100 trials, demonstrating extreme centralization. In contrast, PoTS reduced this dominance: the same node ranked first only 54 times, indicating fairer reward distribution. Statistical analysis showed that as team size increased, skewness and kurtosis of reward distributions decreased, confirming improved equity among participants. PoTS also demonstrated significant energy savings. The total active computation time followed a near $1/N$ scaling trend, reducing energy use by up to 64 times when team size was 64, while preserving consensus integrity. Repeated simulations showed stable reward distributions and system performance, affirming PoTS's robustness. Furthermore, the correlation between performance and reward peaked at 0.90 for team size 16, reflecting an optimal balance between fairness and meritocracy. Overall, PoTS offers a cooperative, energy-efficient alternative to PoW, mitigating centralization risks and promoting equitable participation. These findings validate PoTS as a sustainable and fair consensus mechanism suited for future blockchain systems.
Energy is a fundamental component of modern life, driving nearly all aspects of daily activities. As such, the inability to access energy when needed is a significant issue that requires innovative solutions. In this paper, we propose ED-DAO, a novel fully transparent and community-driven decentralized autonomous organization (DAO) designed to facilitate energy donations. We analyze the energy donation process by exploring various approaches and categorizing them based on both the source of donated energy and funding origins. We propose a novel Hybrid Energy Donation (HED) algorithm, which enables contributions from both external and internal donors. External donations are payments sourced from entities such as charities and organizations, where energy is sourced from the utility grid and prosumers. Internal donations, on the other hand, come from peer contributors with surplus energy. HED prioritizes donations in the following sequence: peer-sourced energy (P2D), utility-grid-sourced energy (UG2D), and direct energy donations by peers (P2PD). By merging these donation approaches, the HED algorithm increases the volume of donated energy, providing a more effective means to address energy poverty. Experiments were conducted on a dataset to evaluate the effectiveness of the proposed method. The results showed that HED increased the total donated energy by at least 0.43% (64 megawatts) compared to the other algorithms (UG2D, P2D, and P2PD).
Blockchain technology is rapidly evolving, with scalability remaining one of its most significant challenges. While various solutions have been proposed and continue to be developed, it is essential to consider the blockchain trilemma -- balancing scalability, security, and decentralization -- when designing new approaches. One promising solution is the zero-knowledge proof (ZKP)-based rollup, implemented on top of Ethereum. However, the performance of these systems is often limited by the efficiency of the ZKP mechanism. This paper explores the performance of ZKP-based rollups, focusing on a solution built using the Hardhat Ethereum development environment. Through detailed analysis, the paper identifies and examines key bottlenecks within the ZKP system, providing insight into potential areas for optimization to enhance scalability and overall system performance.
Zeta Avarikioti, Eleftherios Kokoris Kogias, Ray Neiheiser, Christos Stefo
The security of many Proof-of-Stake (PoS) payment systems relies on quorum-based State Machine Replication (SMR) protocols. While classical analyses assume purely Byzantine faults, real-world systems must tolerate both arbitrary failures and strategic, profit-driven validators. We therefore study quorum-based SMR under a hybrid model with honest, Byzantine, and rational participants. We first establish the fundamental limitations of traditional consensus mechanisms, proving two impossibility results: (1) in partially synchronous networks, no quorum-based protocol can achieve SMR when rational and Byzantine validators collectively exceed $1/3$ of the participants; and (2) even under synchronous network assumptions, SMR remains unattainable if this coalition comprises more than $2/3$ of the validator set. Assuming a synchrony bound $Δ$, we show how to extend any quorum-based SMR protocol to tolerate up to $1/3$ Byzantine and $1/3$ rational validators by modifying only its finalization rule. Our approach enforces a necessary bound on the total transaction volume finalized within any time window $Δ$ and introduces the \emph{strongest chain rule}, which enables efficient finalization of transactions when a supermajority of honest participants provably supports execution. Empirical analysis of Ethereum and Cosmos demonstrates validator participation exceeding the required $5/6$ threshold in over $99%$ of blocks, supporting the practicality of our design. Finally, we present a recovery mechanism that restores safety and liveness after consistency violations, even with up to $5/9$ Byzantine stake and $1/9$ rational stake, guaranteeing full reimbursement of provable client losses.
To address the risks of validator centralization, Proposer-Builder Separation (PBS) was introduced in Ethereum to divide the roles of block building and block proposing, fostering a more equitable and decentralized block production environment. PBS creates a two-sided market in which searchers submit valuable bundles to builders for inclusion in blocks, while builders compete in auctions for block proposals. In this paper, we formulate and analyze a role-selection game that models how profit-seeking participants in PBS strategically choose between acting as searchers or builders, using a co-evolutionary framework to capture the complex interactions and payoff dynamics in this market. Through agent-based simulations, we demonstrate that agents' optimal role-acting as searcher or builder-responds dynamically to the probability of conflict between bundles. Our empirical game-theoretic analysis quantifies the equilibrium frequencies of role selection under different market conditions, revealing that low conflict probabilities lead to equilibria dominated by searchers, while higher probabilities shift equilibrium toward builders. Additionally, bundle conflicts have non-monotonic effects on agent payoffs and strategy evolution. Our results advance the understanding of decentralized block building and provide guidance for designing fairer and more robust block production mechanisms in blockchain systems.
We introduce a zero-knowledge cryptocurrency mixer framework that allows groups of users to set up a mixing pool with configurable governance conditions, configurable deposit delays, and the ability to refund or confiscate deposits if it is suspected that funds originate from crime. Using a consensus process, group participants can monitor inputs to the mixer and determine whether the inputs satisfy the mixer conditions. If a deposit is accepted by the group, it will enter the mixer and become untraceable. If it is not accepted, the verifiers can freeze the deposit and collectively vote to either refund the deposit back to the user, or confiscate the deposit and send it to a different user. This behaviour can be used to examine deposits, determine if they originate from a legitimate source, and if not, return deposits to victims of crime.
The number of blockchain interoperability protocols for transferring data and assets between blockchains has grown significantly. However, no open dataset of cross-chain transactions exists to study interoperability protocols in operation. There is also no tool to generate such datasets and make them available to the community. This paper proposes XChainDataGen, a tool to extract cross-chain data from blockchains and generate datasets of cross-chain transactions (cctxs). Using XChainDataGen, we extracted over 35 GB of data from five cross-chain protocols deployed on 11 blockchains in the last seven months of 2024, identifying 11,285,753 cctxs that moved over 28 billion USD in cross-chain token transfers. Using the data collected, we compare protocols and provide insights into their security, cost, and performance trade-offs. As examples, we highlight differences between protocols that require full finality on the source blockchain and those that only demand soft finality (\textit{security}). We compare user costs, fee models, and the impact of variables such as the Ethereum gas price on protocol fees (\textit{cost}). Finally, we produce the first analysis of the implications of EIP-7683 for cross-chain intents, which are increasingly popular and greatly improve the speed with which cctxs are processed (\textit{performance}), thereby enhancing the user experience. The availability of XChainDataGen and this dataset allows various analyses, including trends in cross-chain activity, security assessments of interoperability protocols, and financial research on decentralized finance (DeFi) protocols.
Federated learning (FL) enables multiple participants to collaboratively train machine learning models while ensuring their data remains private and secure. Blockchain technology further enhances FL by providing stronger security, a transparent audit trail, and protection against data tampering and model manipulation. Most blockchain-secured FL systems rely on conventional consensus mechanisms: Proof-of-Work (PoW) is computationally expensive, while Proof-of-Stake (PoS) improves energy efficiency but risks centralization as it inherently favors participants with larger stakes. Recently, learning-based consensus has emerged as an alternative by replacing cryptographic tasks with model training to save energy. However, this approach introduces potential privacy vulnerabilities, as the training process may inadvertently expose sensitive information through gradient sharing and model updates. To address these challenges, we propose a novel Zero-Knowledge Proof of Training (ZKPoT) consensus mechanism. This method leverages the zero-knowledge succinct non-interactive argument of knowledge proof (zk-SNARK) protocol to validate participants' contributions based on their model performance, effectively eliminating the inefficiencies of traditional consensus methods and mitigating the privacy risks posed by learning-based consensus. We analyze our system's security, demonstrating its capacity to prevent the disclosure of sensitive information about local models or training data to untrusted parties during the entire FL process. Extensive experiments demonstrate that our system is robust against privacy and Byzantine attacks while maintaining accuracy and utility without trade-offs, scalable across various blockchain settings, and efficient in both computation and communication.
We present SmartShards: a new sharding algorithm for improving Byzantine tolerance and churn resistance in blockchains. Our algorithm places a peer in multiple shards to create an overlap. This simplifies cross-shard communication and shard membership management. We describe SmartShards, prove it correct and evaluate its performance. We propose several SmartShards extensions: defense against a slowly adaptive adversary, combining transactions into blocks, fortification against the join/leave attack.
Synchronous consensus protocols offer a significant advantage over their asynchronous and partially synchronous counterparts by providing higher fault tolerance -- an essential benefit in distributed systems, like blockchains, where participants may have incentives to act maliciously. However, despite this advantage, synchronous protocols are often met with skepticism due to concerns about their performance, as the latency of synchronous protocols is tightly linked to a conservative time bound for message delivery. This paper introduces AlterBFT, a new Byzantine fault-tolerant consensus protocol. The key idea behind AlterBFT lies in the new model we propose, called hybrid synchronous system model. The new model is inspired by empirical observations about network behavior in the public cloud environment and combines elements from the synchronous and partially synchronous models. Namely, it distinguishes between small messages that respect time bounds and large messages that may violate bounds but are eventually timely. Leveraging this observation, AlterBFT achieves up to 15$\times$ lower latency than state-of-the-art synchronous protocols while maintaining similar throughput and the same fault tolerance. Compared to partially synchronous protocols, AlterBFT provides higher fault tolerance, higher throughput, and comparable latency.
Abstract With the rapid growth of hyperconnected devices and decentralized data architectures, safeguarding Internet of Things (IoT) transactions is becoming increasingly challenging. Blockchain presents a promising solution, yet its effectiveness depends on the underlying consensus algorithm. Conventional mechanisms, such as Proof of Work and Proof of Stake, are often impractical for resource-constrained IoT environments. To address these limitations, this work introduces a fair and lightweight hybrid consensus algorithm tailored for IoT. The proposed approach minimizes resource demands on the nodes while providing a fair and secure agreement process. Specifically, it utilizes a distributed lottery mechanism to ensure fair block proposals without requiring dedicated hardware. In addition, to enhance trust and establish finality, a reputation-based voting mechanism is incorporated. Finally, we experimentally validated the key features of the proposed consensus algorithm.
Stefano De Angelis, Ivan Visconti, Andrea Vitaletti, Marco Zecchini
Blockchains are among the most powerful technologies to realize decentralized information systems. In order to safely enjoy all guarantees provided by a blockchain, one should maintain a full node, therefore maintaining an updated local copy of the ledger. This allows one to locally verify transactions, states of smart contracts, and to compute any information over them. Unfortunately, for obvious practical reasons, a very large part of blockchain-based information systems consists of users relying on clients that access data stored in blockchains only through servers, without verifying what is received. In notable use cases, the user has application-specific queries that can be answered only by very few servers, sometimes all belonging to the same organization. This clearly re-introduces a single point of failure. In this work we present an architecture allowing superlight clients (i.e., clients that do not want to download the involved transactions) to outsource the computation of a query to a (possibly untrusted) server, receiving a trustworthy answer. Our architecture relies on the power of SNARKs and makes them lighter to compute by using data obtained from full nodes and blockchain explorers, possibly leveraging the existence of smart contracts. The viability of our architecture is confirmed by an experimental evaluation on concrete scenarios. Our work paves the road towards blockchain-based information systems that remain decentralized and reliable even when users rely on common superlight clients (e.g., smartphones).
J Dantas, P Silva, L Fiondella, C Melo · 5 authors
Blockchain technology has emerged, and many previous studies have assessed its performance issues. However, less attention has been paid to the dependability attributes, which have been a critical topic in service provisioning, considering public or private infrastructures. This paper introduces analytical models to assess the availability of private blockchain infrastructure for Hyperledger Fabric-based applications. Furthermore, a case study will be presented to demonstrate the feasibility of the proposed model, which may assist stakeholders in deciding whether to migrate from old to new technology. Some of the obtained results indicate that, unlike most conventional systems, general availability may decrease as new nodes are added to the environment. This phenomenon occurs due to the adopted endorsement policy, which determines the proportion of required nodes to sign the authenticity of a transaction.
The rapid growth of Blockchain and Decentralized Finance (DeFi) has introduced new challenges and vulnerabilities that threaten the integrity and efficiency of the ecosystem. This study identifies critical issues such as Transaction Order Dependence (TOD), Blockchain Extractable Value (BEV), and Transaction Importance Diversity (TID), which collectively undermine the fairness and security of DeFi systems. BEV-related activities, including Sandwich attacks, Liquidations, and Transaction Replay, have emerged as significant threats, collectively generating $540.54 million in losses over 32 months across 11,289 addresses, involving 49,691 cryptocurrencies and 60,830 on-chain markets. These attacks exploit transaction mechanics to manipulate asset prices and extract value at the expense of other participants, with Sandwich attacks being particularly impactful. Additionally, the growing adoption of Blockchain in traditional finance highlights the challenge of TID, where high transaction volumes can strain systems and compromise time-sensitive operations. To address these pressing issues, we propose a novel Distributed Transaction Sequencing Strategy (DTSS), which combines forking mechanisms and the Analytic Hierarchy Process (AHP) to enforce fair and transparent transaction ordering in a decentralized manner. Our approach is further enhanced by an optimization framework and the introduction of the Normalized Allocation Disparity Metric (NADM), which ensures optimal parameter selection for transaction prioritization. Experimental evaluations demonstrate that DTSS effectively mitigates BEV risks, enhances transaction fairness, and significantly improves the security and transparency of DeFi ecosystems. This work is essential for protecting the future of decentralized finance and promoting its integration into global financial systems.
Margarita Capretto, MartÃn Ceresa, Antonio Fernández Anta, Pedro Moreno-Sánchez · 5 authors
Blockchains face a scalability challenge due to the intrinsic throughput limitations of consensus protocols and the limitation in block sizes due to decentralization. An alternative to improve the number of transactions per second is to use Layer 2 (L2) rollups. L2s perform most computations offchain using blockchains (L1) minimally under-the-hood to guarantee correctness. A sequencer receives offchain L2 transaction requests, batches them, and commits compressed or hashed batches to L1. Hashing offers much better compression but requires a data availability committee (DAC) to translate hashes back into their corresponding batches. Current L2s consist of a centralized sequencer which receives and serializes all transactions and an optional DAC. Centralized sequencers can undesirably influence L2s evolution. We propose in this paper a fully decentralized implementation of a service that combines (1) a sequencer that posts hashes to the L1 blockchain and (2) the data availability committee that reverses the hashes. We call the resulting service a (decentralized) arranger. Our decentralized arranger is based on Set Byzantine Consensus (SBC), a service where participants can propose sets of values and consensus is reached on a subset of the union of the values proposed. We extend SBC for our fully decentralized arranger. Our main contributions are (1) a formal definition of arrangers; (2) two implementations, one with a centralized sequencer and another with a fully decentralized algorithm, with their proof of correctness; and (3) empirical evidence that our solution scales by implementing all building blocks necessary to implement a correct server.
The increasing adoption of blockchain technology has led to a growing demand for higher transaction throughput. Traditional blockchain platforms, such as Ethereum, execute transactions sequentially within each block, limiting scalability. Parallel execution has been proposed to enhance performance, but existing approaches either impose strict dependency annotations, rely on conservative static analysis, or suffer from high contention due to inefficient state management. Moreover, even when transaction execution is parallelized at the upper layer, storage operations remain a bottleneck due to sequential state access and I/O amplification. In this paper, we propose Reddio, a batch-based parallel transaction execution framework with asynchronous storage. Reddio processes transactions in parallel while addressing the storage bottleneck through three key techniques: (i) direct state reading, which enables efficient state access without traversing the Merkle Patricia Trie (MPT); (ii) asynchronous parallel node loading, which preloads trie nodes concurrently with execution to reduce I/O overhead; and (iii) pipelined workflow, which decouples execution, state reading, and storage updates into overlapping phases to maximize hardware utilization.
Parallel execution of smart contract transactions in large multicore architectures is critical for higher efficiency and improved throughput. The main bottleneck for maximizing the throughput of a node through parallel execution is transaction conflict resolution: when two transactions interact with the same data, like an account balance, their order matters. Imagine one transaction sends tokens from account A to account B, and another tries to send tokens from account B to account C. If the second transaction happens before the first one, the token balance in account B might be wrong, causing the entire system to break. Conflicts like these must be managed carefully, or you end up with an inconsistent, unusable blockchain state. Traditional software transactional memory (STM) has been identified as a possible abstraction for the concurrent execution of transactions within a block, with Block-STM pioneering its application for efficient blockchain transaction processing on multicore validator nodes. This paper presents a parallel execution methodology that leverages conflict specification information of the transactions for block transactional memory (BTM) algorithms. Our experimental analysis, conducted over synthetic transactional workloads and real-world blocks, demonstrates that BTMs leveraging conflict specifications outperform their plain counterparts on both EVM and MoveVM. Our proposed BTM implementations achieve up to 1.75x speedup over sequential execution and outperform the state-of-the-art Parallel-EVM (PEVM) execution by up to 1.33x across synthetic workloads.
Sergio Demian Lerner, Martin Jonas, Ariel Futoransky
The BitVM and BitVMX protocols have long relied on inefficient one-time signature (OTS) schemes like Lamport and Winternitz for signing program inputs. These schemes exhibit significant storage overheads, hindering their practical application. This paper introduces ESSPI, an optimized method leveraging ECDSA/Schnorr signatures to sign the BitVMX program input. With Schnorr signatures we achieve an optimal 1:1 data expansion, compared to the current known best ratio of 1:200 based on Winternitz signatures. To accomplish this we introduce 4 innovations to BitVMX: (1) a modification of the BitVMX CPU, adding a challengeable hashing core to it, (2) a new partition-based search to detect fraud during hashing, (3) a new enhanced transaction DAG with added data-carrying transactions with a fraud-verifying smart-contract and (4) a novel timelock-based method for proving data availability to Bitcoin smart contracts. The enhanced BitVMX protocol enables the verification of uncompressed inputs such as SPV proofs, NiPoPoWs, or longer computation integrity proofs, such as STARKs.
Many blockchain-based decentralized services require their validators (operators) to deposit stake (collateral), which is forfeited (slashed) if they misbehave. Restaking networks let validators secure multiple services by reusing stake. These networks have quickly gained traction, leveraging over \$20 billion in stake. However, restaking introduces a new attack vector where validators can coordinate to misbehave across multiple services simultaneously, extracting digital assets while forfeiting their stake only once. Previous work focused either on preventing coordinated misbehavior or on protecting services if all other services are Byzantine and might unjustly cause slashing due to bugs or malice. The first model overlooks how a single Byzantine service can collapse the network, while the second ignores shared-stake benefits. To bridge the gap, we analyze the system as a strategic game of coordinated misbehavior, when a given fraction of the services are Byzantine. We introduce elastic restaking networks, where validators can allocate portions of their stake that may cumulatively exceed their total stake, and when allocations are lost, the remaining stake stretches to cover remaining allocations. We show that elastic networks exhibit superior robustness compared to previous approaches, and demonstrate a synergistic effect where an elastic restaking network enhances its blockchain's security, contrary to community concerns of an opposite effect in existing networks. We then design incentives for tuning validators' allocations. Our elastic restaking system and incentive design have immediate practical implications for deployed restaking networks.
Federated Learning (FL) has recently emerged as a collaborative learning paradigm that can train a global model among distributed participants without raw data exchange to satisfy varying requirements. However, there remain several challenges in managing FL in a decentralized environment, where potential candidates exhibit varying motivation levels and reliability in the FL process management: 1) reconfiguring and automating diverse FL workflows are challenging, 2) difficulty in incentivizing potential candidates with high-quality data and high-performance computing to join the FL, and 3) difficulty in ensuring reliable system operations, which may be vulnerable to various malicious attacks from FL participants. To address these challenges, we focus on the workflow-based methods to automate diverse FL pipelines and propose a novel approach to facilitate reliable FL system operations with robust mechanism design and blockchain technology by considering a contribution model, fair committee selection, dynamic reputation updates, reward and penalty methods, and contract theory. Moreover, we study the optimality of contracts to guide the design and implementation of smart contracts that can be deployed in blockchain networks. We perform theoretical analysis and conduct extensive simulation experiments to validate the proposed approach. The results show that our incentive mechanisms are feasible and can achieve fairness in reward allocation in unreliable environment settings.
Mohammad Mussadiq Jalalzai, Kushal Babel, Jovan Komatovic, Tobias Klenze · 9 authors
This paper introduces MonadBFT, a novel Byzantine Fault Tolerant (BFT) consensus protocol that advances both performance and robustness. MonadBFT is implemented as the consensus protocol in the Monad blockchain. As a HotStuff-family protocol, MonadBFT has linear message complexity in the common case and is optimistically responsive, operating as quickly as the network allows. A central feature of MonadBFT is its tail-forking resistance. In pipelined BFT protocols, when a leader goes offline, the previous proposal is abandoned. Malicious leaders can exploit this tail-forking behavior as a form of Maximal Extractable Value (MEV) attack by deliberately discarding their predecessor's block, depriving that proposer of rewards and enabling transaction reordering, censorship or theft. MonadBFT prevents such tail-forking attacks, preserving both fairness and integrity in transaction execution. Another related feature of MonadBFT is its notion of speculative finality, which enables parties to execute ordered transactions after a single round (i.e., a single view), with reverts occurring only in the rare case of provable leader equivocation. This mechanism reduces user-perceived latency. Additionally, we introduce the leader fault isolation property, which ensures that the protocol can quickly recover from a failure. To our knowledge, no prior pipelined, leader-based BFT consensus protocol combines all of these properties in a single design.
Order fairness in distributed ledgers refers to properties that relate the order in which transactions are sent or received to the order in which they are eventually finalized, i.e., totally ordered. The study of such properties is relatively new and has been especially stimulated by the rise of Maximal Extractable Value (MEV) attacks in blockchain environments. Indeed, in many classical blockchain protocols, leaders are responsible for selecting the transactions to be included in blocks, which creates a clear vulnerability and opportunity for transaction order manipulation. Unlike blockchains, DAG-based ledgers allow participants in the network to independently propose blocks, which are then arranged as vertices of a directed acyclic graph. Interestingly, leaders in DAG-based ledgers are elected only after the fact, once transactions are already part of the graph, to determine their total order. In other words, transactions are not chosen by single leaders; instead, they are collectively validated by the nodes, and leaders are only elected to establish an ordering. This approach intuitively reduces the risk of transaction manipulation and enhances fairness. In this paper, we aim to quantify the capability of DAG-based ledgers to achieve order fairness. To this end, we define new variants of order fairness adapted to DAG-based ledgers and evaluate the impact of an adversary capable of compromising a limited number of nodes (below the one-third threshold) to reorder transactions. We analyze how often our order fairness properties are violated under different network conditions and parameterizations of the DAG algorithm, depending on the adversary's power. Our study shows that DAG-based ledgers are still vulnerable to reordering attacks, as an adversary can coordinate a minority of Byzantine nodes to manipulate the DAG's structure.