Time Is Money: Incentivized Causal Transaction Ordering

• URL: http://arxiv.org/abs/2607.11496v1
• Date: Mon, 13 Jul 2026 12:49:34 GMT
• Title: Time Is Money: Incentivized Causal Transaction Ordering
• Abstract summary: We present PRECEDE, a mechanism-design approach that enforces transaction causality by removing the economic incentive to front-run.<n> PRECEDE can be directly deployed in any censorship-resistant blockchain with a simple change to its transaction ordering mechanism.
• Score: 9.096175121355737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Front-running is a subtle and persistent problem for blockchains. A blockchain is a stateful virtual machine executing instructions called transactions. Users earn rewards by publishing functional transactions essential to the system. Attackers observe these transactions and publish their own ahead of the users', seizing the reward and eroding users' incentive to publish functional transactions. Preventing front-running means enforcing causality: If an attacker receives transaction tx_A and then publishes transaction tx_B, then tx_A must be ordered before tx_B. However, this causality is only observed by the attacker. Practical systems order transactions by bid amount, so transactions willing to pay more get executed first, but this only results in a bidding war eroding users' rewards. Though numerous ordering approaches have been proposed, none achieves causality, leaving users vulnerable to front-running. We present PRECEDE, a mechanism-design approach that enforces transaction causality by removing the economic incentive to front-run. PRECEDE orders transactions by a power-weighted randomized lottery, whose winning probability grows super-linearly in the bid. The user's strategy of publishing a transaction with a deterring bid forms an equilibrium where the attacker refrains from competing. Moreover, PRECEDE prevents the prominent sandwich attack, which relies on front-running. PRECEDE can be directly deployed in any censorship-resistant blockchain with a simple change to its transaction ordering mechanism.

Related papers

• A Tattered Cloak of Invisibility: Measuring Anonymity Loss in Railgun on Ethereum [3.47720456613335]
  We study behavioral leakage in Railgun on cryptocurrency mixers.<n>We consider five sources of leakage: characteristic timing patterns, address reuse, proximity in the transaction graph that preserve distinctive digit patterns across transaction values, and knapsack type matches in which groups of transaction amounts add up in revealing ways.
  arXiv  Detail & Related papers  (2026-06-24T15:03:56Z)
• Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection [55.18577006122169]
  We propose a novel framework termed textbfTEmporal textbfMotif-aware textbfGraph textbfTest-textbfTime textbfAdaptation (textbfTEMG-TTA)<n>First, we comprehensively capture the 3-node temporal motif distribution of each active address using an efficient computational mechanism.<n>Second, we design a simple yet effective test-time adaptation strategy to facilitate the sharing of common patterns between training and testing graphs.
  arXiv  Detail & Related papers  (2026-05-28T07:43:20Z)
• Sedna: Sharding transactions in multiple concurrent proposer blockchains [42.71280924071485]
  We present Sedna, a user-facing protocol that replaces naive transaction replication with verifiable, rateless coding.<n>We prove Sedna guarantees liveness and emphuntil-decode privacy, significantly reducing MEV exposure.
  arXiv  Detail & Related papers  (2025-12-18T20:12:55Z)
• Epass: Efficient and Privacy-Preserving Asynchronous Payment on Blockchain [39.093148638790346]
  Buy Now Pay Later (BNPL) is a rapidly proliferating e-commerce model, offering consumers to get the product immediately and defer payments.<n>Emerging blockchain technologies endow BNPL platforms with digital currency transactions, allowing BNPL platforms to integrate with digital wallets.<n>However, the transparency of transactions causes critical privacy concerns because malicious participants may derive consumers' financial statuses from on-chain asynchronous payments.
  arXiv  Detail & Related papers  (2025-06-11T04:32:54Z)
• Demystifying Private Transactions and Their Impact in PoW and PoS Ethereum [43.548299433042835]
  Private transactions, a specialized transaction type employed to evade public Peer-to-Peer (P2P) network broadcasting, remain largely unexplored.<n>We analyze large-scale datasets comprising 14,810,392 private transactions within a 15.5-month Proof-of-Work (PoW) dataset and 30,062,232 private transactions within a 15.5-month Proof-of-Stake (PoS) dataset.
  arXiv  Detail & Related papers  (2025-03-30T16:45:18Z)
• BlockScan: Detecting Anomalies in Blockchain Transactions [16.73896087813861]
  BlockScan is a customized Transformer for anomaly detection in blockchain transactions.<n>This work sets a new benchmark for applying Transformer-based approaches in blockchain data analysis.
  arXiv  Detail & Related papers  (2024-10-05T05:11:34Z)
• Transaction Capacity, Security and Latency in Blockchains [45.776687601070705]
  We analyze how secure a block is after the block becomes $k$-deep, i.e., security-latency, for Nakamoto consensus.<n>We connect the security-latency analysis to sustainable transaction rate of the queue system.
  arXiv  Detail & Related papers  (2024-02-15T17:43:13Z)
• Designing Redistribution Mechanisms for Reducing Transaction Fees in Blockchains [10.647087323578477]
  Transaction Fee Mechanisms (TFMs) determine which user transactions to include in blocks and determine their payments. We propose Transaction Fee Redistribution Mechanisms (TFRMs) -- redistributing VCG payments as rebates to minimize transaction fees. Our results show that TFRMs provide a promising new direction for reducing transaction fees in public blockchains.
  arXiv  Detail & Related papers  (2024-01-24T07:09:32Z)
• FRAD: Front-Running Attacks Detection on Ethereum using Ternary Classification Model [3.929929061618338]
  Front-running attacks, a unique form of security threat, pose significant challenges to the integrity of blockchain transactions. In these attack scenarios, malicious actors monitor other users' transaction activities, then strategically submit their own transactions with higher fees. We introduce a novel detection method named FRAD (Front-Running Attacks Detection on using Ternary Classification Model) Our experimental validation reveals that the Multilayer Perceptron (MLP) classifier offers the best performance in detecting front-running attacks, achieving an impressive accuracy rate of 84.59% and F1-score of 84.60%.
  arXiv  Detail & Related papers  (2023-11-24T14:42:29Z)
• Towards Stronger Blockchains: Security Against Front-Running Attacks [10.220888127527152]
  We show that total ordering is not strong enough to preserve application semantics under Byzantine fault model. This is due to the fact that malicious miners and clients can collaborate to add their own transactions ahead of correct clients' transactions in order to gain application level and financial advantages. We propose preserving causality preserving total order as a solution.
  arXiv  Detail & Related papers  (2023-11-17T00:50:49Z)
• Masquerade: Simple and Lightweight Transaction Reordering Mitigation in Blockchains [5.690884793952696]
  We propose an MEV aware protocol design called Masquerade to increase user satisfaction and confidence in the system. We introduce the notion of a "token" to mitigate the actions taken by an adversary in an attack scenario.
  arXiv  Detail & Related papers  (2023-08-29T14:42:43Z)
• Blockchain Large Language Models [65.7726590159576]
  This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions. The proposed tool, BlockGPT, generates tracing representations of blockchain activity and trains from scratch a large language model to act as a real-time Intrusion Detection System.
  arXiv  Detail & Related papers  (2023-04-25T11:56:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.