Be Selfish and Avoid Dilemmas: Fork After Withholding (FAW) Attacks on Bitcoin
Yujin Kwon, Dohyun Kim, Yunmok Son, Eugene Y. Vasserman, Yongdae Kim
摘要
In the Bitcoin system, participants are rewarded for solving cryptographic puzzles. In order to receive more consistent rewards over time, some participants organize mining pools and split the rewards from the pool in proportion to each participant's contribution. However, several a acks threaten the ability to participate in pools. e block withholding (BWH) a ack makes the pool reward system unfair by le ing malicious participants receive unearned wages while only pretending to contribute work. When two pools launch BWH a acks against each other, they encounter the miner's dilemma: in a Nash equilibrium, the revenue of both pools is diminished. In another a ack called sel sh mining, an a acker can unfairly earn extra rewards by deliberately generating forks. In this paper, we propose a novel a ack called a fork a er withholding (FAW) a ack. FAW is not just another a ack. e reward for an FAW a acker is always equal to or greater than that for a BWH a acker, and it is usable up to four times more o en per pool than in BWH a ack. When considering multiple pools -the current state of the Bitcoin network -the extra reward for an FAW a ack is about 56% more than that for a BWH a ack. Furthermore, when two pools execute FAW a acks on each other, the miner's dilemma may not hold: under certain circumstances, the larger pool can consistently win. More importantly, an FAW a ack, while using intentional forks, does not su er from practicality issues, unlike sel sh mining. We also discuss partial countermeasures against the FAW a ack, but nding a cheap and e cient countermeasure remains an open problem. As a result, we expect to see FAW a acks among mining pools. CCS CONCEPTS •Security and privacy → Distributed systems security; Economics of security and privacy;
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper12
- The Gap GameItay Tsabary, Ittay EyalCCS 2018 · 被引用 119 次
- Power Adjusting and Bribery Racing: Novel Mining Attacks in the Bitcoin SystemShang Gao, Zecheng Li, Zhe Peng, Bin XiaoCCS 2019 · 被引用 81 次
- Bitcoin vs. Bitcoin Cash: Coexistence or Downfall of Bitcoin Cash?Yujin Kwon, Hyoungshick Kim, Jinwoo Shin, Yongdae KimS&P 2019 · 被引用 51 次
- Do the Rich Get Richer? Fairness Analysis for Blockchain IncentivesYuming Huang, Jing Tang, Qianhao Cong, Andrew Lim 等SIGMOD 2021 · 被引用 40 次
- How Hard is Takeover in DPoS Blockchains? Understanding the Security of Coin-based Voting GovernanceChao Li, Balaji Palanisamy, Runhua Xu, Li Duan 等CCS 2023 · 被引用 17 次
它引用的顶会 Paper5
- On the Security and Performance of Proof of Work BlockchainsArthur Gervais, Ghassan O. Karame, Karl Wüst, Vasileios Glykantzis 等CCS 2016 · 被引用 1,668 次
- A Secure Sharding Protocol For Open BlockchainsLoi Luu, Viswesh Narayanan, Chaodong Zheng, Kunal Baweja 等CCS 2016 · 被引用 1,392 次
- Enhancing Bitcoin Security and Performance with Strong Consistency via Collective SigningEleftherios Kokoris-Kogias, Philipp Jovanovic, Nicolas Gailly, Ismail Khoffi 等USENIX Security 2016 · 被引用 769 次
- On the Instability of Bitcoin Without the Block RewardMiles Carlsten, Harry A. Kalodner, S. Matthew Weinberg, Arvind NarayananCCS 2016 · 被引用 387 次
- SmartPool: Practical Decentralized Pooled MiningLoi Luu, Yaron Velner, Jason Teutsch, Prateek SaxenaUSENIX Security 2017 · 被引用 144 次
相关 Paper
- Fairness Matters: A Tit-For-Tat Strategy Against Selfish MiningWeijie Sun, Zihuan Xu, Lei ChenVLDB 2022 · 被引用 8 次
- Forking the RANDAO: Manipulating Ethereum's Distributed Randomness BeaconÁbel Nagy, János Tapolcai, István András Seres, Bence LadóczkiCCS 2025 · 被引用 2 次
- Modeling the Impact of Network Connectivity on Consensus Security of Proof-of-Work BlockchainYang Xiao, Ning Zhang, Wenjing Lou, Y. Thomas HouINFOCOM 2020 · 被引用 52 次
- BDoS: Blockchain Denial-of-ServiceMichael Mirkin, Yan Ji, Jonathan Pang, Ariah Klages-Mundt 等CCS 2020 · 被引用 1 次
- SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with Deep Reinforcement LearningCharlie Hou, Mingxun Zhou, Yan Ji, Phil Daian 等NDSS 2021
