On the Strategy and Behavior of Bitcoin Mining with N-attackers

Liu, Hanqing; Ruan, Na; Du, Rongtian; Jia, Weijia

doi:10.1145/3196494.3196512

Cited by 38 publications

(23 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A similar trend in the HM's mining reward with respect to the HM's mining power is also observed and shown in Figure 1(b). As shown by Liu et al [10], our observation has also revealed a similar underlying cause of the trend of HM/SM's mining reward. Generally, the higher the number of miners, the less mining power each of them has.…”

Section: Fixed Strategy Miningsupporting

confidence: 87%

“…In particular, most works so far studied SM or similar strategies in a setting with one malicious miner and one non-malicious miner [6,7,9,12,13,15]. To our knowledge, there is a small-scale study which was recently conducted in parallel [10]. Compared to their work, our findings are more robust due to a large number of malicious miners in the system and a fair treatment of the underlying network in our experiment.…”

Section: Related Workmentioning

confidence: 69%

See 1 more Smart Citation

Selfish Mining in Proof-of-Work Blockchain with Multiple Miners: An Empirical Evaluation

Leelavimolsilp

Nguyễn

Stein

et al. 2019

PRIMA 2019: Principles and Practice of Multi-Agent Systems

View full text Add to dashboard Cite

Proof-of-Work blockchain, despite its numerous benefits, is still not an entirely secure technology due to the existence of Selfish Mining (SM) strategies that can disrupt the system and its mining economy. While the effect of SM has been studied mostly in a two-miners scenario, it has not been investigated in a more practical context where there are multiple malicious miners individually performing SM. To fill this gap, we carry out an empirical study that separately accounts for different numbers of SM miners (who always perform SM) and strategic miners (who choose either SM or Nakamoto's mining protocol depending on which maximises their individual mining reward). Our result shows that SM is generally more effective as the number of SM miners increases, however its effectiveness does not vary in the presence of a large number of strategic miners. Under specific mining power distributions, we also demonstrate that multiple miners can perform SM and simultaneously gain higher mining rewards than they should. Surprisingly, we also show that the more strategic miners there are, the more robust the systems become. Since blockchain miners should naturally be seen as self-interested strategic miners, our findings encourage blockchain system developers and engineers to attract as many miners as possible to prevent SM and similar behaviour.

show abstract

Section: Fixed Strategy Miningsupporting

confidence: 87%

Section: Related Workmentioning

confidence: 69%

Selfish Mining in Proof-of-Work Blockchain with Multiple Miners: An Empirical Evaluation

Leelavimolsilp

Nguyễn

Stein

et al. 2019

PRIMA 2019: Principles and Practice of Multi-Agent Systems

View full text Add to dashboard Cite

show abstract

“…Nayak et al 15 prove that stubborn attacks improve about 13.94% revenues compared with selfish mining attacks. Liu et al extend SM to

N

attackers and propose a new attacking model: publish‐n 16 . Finally, they compare its validity with selfish mining and stubborn attacks.…”

Section: Introductionmentioning

confidence: 99%

Semi‐selfish mining based on hidden Markov decision process

Wang

Yang

et al. 2021

Int J Intell Syst

View full text Add to dashboard Cite

Selfish mining attacks sabotage the blockchain systems by utilizing the vulnerabilities of consensus mechanism. The attackers' main target is to obtain higher revenues compared with honest parties. More specifically, the essence of selfish mining is to waste the power of honest parties by generating a private chain. However, these attacks are not practical due to high forking rate. The honest parties may quit the blockchain system once they detect the abnormal forking rate, which impairs their revenues. While selfish mining attacks make no sense anymore with the honest parties' departure. Therefore, selfish miners need to restrain when launch selfish mining attacks such that the forking rate is not preposterously higher than normal level. The crux is how to illustrate the attacks toward the view of honest parties, who are blind to the private chain. Generally, previous works, especially those using Markov decision processes, stress on the increment of attackers' revenues, while overlooking the detection on forking rate. In this paper, we propose, to maintain the benefit from selfish mining, an improved selfish mining based on hidden Markov decision processes (SMHMDP). To reduce the forking rate, we also relax the behaviors of selfish miners (also known as semi-selfish miners), who mine on the private chain, to mine on public chain with a small

show abstract

“…This centralization of power, runs counter to the spirit of decentralized cryptocurrencies. Large pools getting larger and small pools getting smaller will ultimately lead to an environment prone harmful attacks [21], [22], [25]. Our model also suggests how this "aggregation" effect can mitigated.…”

Section: B Our Contributionmentioning

confidence: 95%

“…In particular, a number of identified attacks directly concern centralization of mining power. These include double spending [1], [18]- [20], block withholding [8], [21], [22], selfish mining [7], [23] among others [4], [24], [25]. A number of desirable blockchain properties can be hampered due to incompatible mining incentives.…”

Section: A Related Workmentioning

confidence: 99%

Time-Dependent Decision-Making and Decentralization in Proof-of-Work Cryptocurrencies

Zolotavkin

García

Liu

2019

2019 IEEE 32nd Computer Security Foundations Symposium (CSF)

View full text Add to dashboard Cite

Pool mining is a common way to reduce income variance for miners in Proof of Work Cryptocurrencies. A vast majority of mining does happen in pools, where a popular scheme to distribute rewards is Pay per last N Shares (PPLNS). In PPLNS and related schemes, miners are frequently making decisions whose rewards are not immediate and will only manifest in the future. This implies that models of inter-temporal utility are relevant when considering the incentives of miners. We show that when including these features of human behaviour in models of rational pool miners, the conditions that lead to decentralisation are hampered because larger pools may be more attractive to miners. We present a new game theoretical model of PPLNS where rational miners have time preferences. In this setup, the incentives of miners to work for a pool depend on the initial distribution of power between mining pools, as well as the specific details of how time is discounted. Agents jumping to larger pools face a trade-off between reducing the expected payoff from their shares in their current pool, or getting faster rewards in the future by joining a larger pool. We consider a case where pools of different mining power have the same size of reward window N. According to our study, in equilibrium larger pools have a tendency to accumulate a disproportionate share of the network power at the expense of smaller pools. This outcome is prevalent over a large range of realistic model parameters. Our model shows that PPLNS may be harmful to the decentralised governance of cryptocurrencies. A way to ameliorate these negative effects, is to encourage pools to have diverse window sizes, or use different reward mechanisms. Doing this in a decentralised fashion is an open challenge.

show abstract

On the Strategy and Behavior of Bitcoin Mining with N-attackers

Cited by 38 publications

References 7 publications

Selfish Mining in Proof-of-Work Blockchain with Multiple Miners: An Empirical Evaluation

Selfish Mining in Proof-of-Work Blockchain with Multiple Miners: An Empirical Evaluation

Semi‐selfish mining based on hidden Markov decision process

Time-Dependent Decision-Making and Decentralization in Proof-of-Work Cryptocurrencies

Contact Info

Product

Resources

About