Undetectable Selfish Mining

• URL: http://arxiv.org/abs/2309.06847v2
• Date: Sun, 4 Feb 2024 23:08:31 GMT
• Title: Undetectable Selfish Mining
• Authors: Maryam Bahrani, S. Matthew Weinberg,
• Abstract summary: A strategic Bitcoin miner may profit by deviating from the intended Bitcoin protocol. We develop a selfish mining variant that is provably *statistically undetectable* We show that our strategy is strictly profitable for attackers with $38.2% ll 50%$ of the total hashrate.
• Score: 4.625489011466493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\beta'$. We propose a selfish mining strategy that instead produces orphaned blocks at each height independently with probability $\beta > \beta'$. 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 $\beta'$).

Related papers

• The Latency Price of Threshold Cryptosystem in Blockchains [52.359230560289745]
  We study the interplay between threshold cryptography and a class of blockchains that use Byzantine-fault tolerant (BFT) consensus protocols. Existing approaches for threshold cryptosystems introduce a latency overhead of at least one message delay for running the threshold cryptographic protocol. We propose a mechanism to eliminate this overhead for blockchain-native threshold cryptosystems with tight thresholds.
  arXiv  Detail & Related papers  (2024-07-16T20:53:04Z)
• Targeted Nakamoto: A Bitcoin Protocol to Balance Network Security and Energy Consumption [0.0]
  Targeted Nakamoto is a Proof-of-Work protocol augmentation that incentivizes miners to hone in on a target hashrate interval. When hashrate is above target a ceiling is placed on the block reward a miner can receive. When hashrate is below target a floor is placed underneath the miner's block reward.
  arXiv  Detail & Related papers  (2024-05-23T22:26:25Z)
• 51% Attack via Difficulty Increase with a Small Quantum Miner [0.9208007322096532]
  We present a strategy for a single quantum miner with relatively low hashing power. Most proof-of-work cryptocurrencies, including Bitcoin, are vulnerable to our attack.
  arXiv  Detail & Related papers  (2024-03-12T18:45:29Z)
• Cobalt: Optimizing Mining Rewards in Proof-of-Work Network Games [6.052883613180156]
  A key factor affecting mining rewards earned is the connectivity between miners in the peer-to-peer network. We formulate the problem of deciding whom to connect to for miners as a bandit problem. A key contribution of our work is the use of network coordinates based model for learning the network structure within the bandit algorithm.
  arXiv  Detail & Related papers  (2023-07-10T16:50:58Z)
• Nik Defense: An Artificial Intelligence Based Defense Mechanism against Selfish Mining in Bitcoin [1.160208922584163]
  Bitcoin mining's protocol is not incentive-compatible. Some nodes with high computational power can obtain more revenue than their fair share. We propose an artificial intelligence-based defense against selfish mining attacks.
  arXiv  Detail & Related papers  (2023-01-26T23:30:44Z)
• Refined Bitcoin Security-Latency Under Network Delay [35.16231062731263]
  We study how secure a block is after it becomes $k$-deep in the chain. We analyze the race between adversarial and honest chains in three different phases. We find the probability distribution of the growth of the adversarial chains under models similar to those in [Guo, Ren; AFT 2022] when a target block becomes $k$-deep in the chain.
  arXiv  Detail & Related papers  (2022-12-02T18:54:30Z)
• Partial Selfish Mining for More Profits [21.636578888742477]
  Mining attacks aim to gain an unfair share of extra rewards in the blockchain mining. In this paper, we propose a new and feasible Partial Selfish Mining (PSM) attack. We show that PSM attackers can be more profitable than selfish miners under a certain range of mining power and network conditions.
  arXiv  Detail & Related papers  (2022-07-27T11:58:38Z)
• Token Spammers, Rug Pulls, and SniperBots: An Analysis of the Ecosystem of Tokens in Ethereum and in the Binance Smart Chain (BNB) [50.888293380932616]
  We study the ecosystem of the tokens and liquidity pools. We find that about 60% of tokens are active for less than one day. We estimate that 1-day rug pulls generated $240 million in profits.
  arXiv  Detail & Related papers  (2022-06-16T14:20:19Z)
• BABD: A Bitcoin Address Behavior Dataset for Address Behavior Pattern Analysis [36.42552617883664]
  We build a dataset comprising Bitcoin transactions between 12 July 2019 and 26 May 2021. This dataset contains 13 types of Bitcoin addresses, 5 categories of indicators with 148 features, and 544,462 labeled data. We use our proposed dataset on common machine learning models, namely: k-nearest neighbors algorithm, decision tree, random forest, multilayer perceptron, and XGBoost.
  arXiv  Detail & Related papers  (2022-04-10T06:46:51Z)
• Quantum Multi-Solution Bernoulli Search with Applications to Bitcoin's Post-Quantum Security [67.06003361150228]
  A proof of work (PoW) is an important cryptographic construct enabling a party to convince others that they invested some effort in solving a computational task. In this work, we examine the hardness of finding such chain of PoWs against quantum strategies. We prove that the chain of PoWs problem reduces to a problem we call multi-solution Bernoulli search, for which we establish its quantum query complexity.
  arXiv  Detail & Related papers  (2020-12-30T18:03:56Z)
• Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack [41.060507338755784]
  This paper investigates the attack model where an adversary attacks with a certain probability at each round, and its attack value can be arbitrary and unbounded if it attacks. We propose a novel sample median-based and exploration-aided UCB algorithm (called med-E-UCB) and a median-based $epsilon$-greedy algorithm (called med-$epsilon$-greedy) Both algorithms are provably robust to the aforementioned attack model. More specifically we show that both algorithms achieve $mathcalO(log T)$ pseudo-regret (i.e
  arXiv  Detail & Related papers  (2020-02-17T19:21:08Z)

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.