American Institute of Mathematical Sciences

Advanced
November  2018, 1(4): 393-414. doi: 10.3934/mfc.2018020

A survey: Reward distribution mechanisms and withholding attacks in Bitcoin pool mining

Saide Zhu 1, , Wei Li 1,, , Hong Li 2, , Chunqiang Hu 3, and  Zhipeng Cai 1,

1. 

Department of Computer Science, Georgia State University, Atlanta, GA, USA
2. 

Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
3. 

School of Big Data & Software Engineering, Chongqing University, Chongqing, China

Corresponding author: Wei Li

Received  August 2018 Revised  October 2018 Published  December 2018

The past three years have seen the rapid increase of Bitcoin difficulty, which has led to a substantial variance in solo mining. As a result, miners tend to join a large open pool to get a more stable reward. Nowadays, mining pools take up over 98% of Bitcoins total computation power. In a sense, this is a manifestation of Bitcoin that tends to be centralized. Thus, researchers have shown an increased interest in pool mining payoff and security. The purpose of this paper is to review and summarize recent research in Bitcoin pool mining system. We first introduce several common reward distribution schemes, and analyze their advantages and disadvantages with some improvement mechanisms; In the second section, to address pool security problems, we examined the practical utility of some existing and potential attack strategies. To study those malicious attack in details, several defense methods are collected. Finally, we make an outlook on Bitcoin future.

Keywords: Bitcoin, pool mining, reward distribution, withholding attack.
Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.
Citation: Saide Zhu, Wei Li, Hong Li, Chunqiang Hu, Zhipeng Cai. A survey: Reward distribution mechanisms and withholding attacks in Bitcoin pool mining. Mathematical Foundations of Computing, 2018, 1 (4) : 393-414. doi: 10.3934/mfc.2018020
References:
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show all references

References:
[1]

S. Bag, S. Ruj and K. Sakurai, Bitcoin block withholding attack: Analysis and mitigation, IEEE Trans. Information Forensics and Security, 12 (2017), 1967-1978.  doi: 10.1109/TIFS.2016.2623588.  Google Scholar

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[4]

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N. T. Courtois and L. Bahack, On subversive miner strategies and block withholding attack in bitcoin digital currency, CoRR, vol. abs/1402.1718, 2014. [Online]. Available: arXiv: 1402.1718 Google Scholar

[9]

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[10]

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[16]

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[17]

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[18]

E. Heilman, One weird trick to stop selfish miners: Fresh bitcoins, a solution for the honest miner, in Financial Cryptography and Data Security - FC 2014 Workshops, BITCOIN and WAHC 2014, Christ Church, Barbados, March 7, 2014, Revised Selected Papers, 2014, 161–162. doi: 10.1007/978-3-662-44774-1_12.  Google Scholar

[19]

C. Hu, W. Li, X. Cheng, J. Yu, S. Wang and R. Bie, A secure and verifiable access control scheme for big data storage in clouds, IEEE Transactions on Big Data (Early Access), 4 (2018), 341-355.  doi: 10.1109/TBDATA.2016.2621106.  Google Scholar

[20]

Q. Hu, S. Wang, C. Hu, J. Huang, W. Li and X. Cheng, Messages in a concealed bottle: Achieving query content privacy with accurate location-based services, to appear in IEEE Transactions on Vehicular Technology, 67 (2018), 7698-7711.  doi: 10.1109/TVT.2018.2838041.  Google Scholar

[21]

G. O. Karame, E. Androulaki and S. Capkun, Double-spending fast payments in bitcoin, in the ACM Conference on Computer and Communications Security, CCS'12, Raleigh, NC, USA, October 16-18, 2012, 2012, 906–917. doi: 10.1145/2382196.2382292.  Google Scholar

[22]

A. Kothapalli, A. Miller and N. Borisov, Smartcast: An incentive compatible consensus protocol using smart contracts, in International Conference on Financial Cryptography and Data Security, 2017, 536–552. Google Scholar

[23]

J. A. Kroll, I. C. Davey and E. W. Felten, The economics of bitcoin mining, or bitcoin in the presence of adversaries, in Proceedings of WEIS, 2013 (2013), p. 11. Google Scholar

[24]

W. Li, M. Larson, C. Hu, R. Li, X. Cheng and R. Bie, Secure multi-unit sealed first-price auction mechanisms, Security and Communication Netowrks SI on Cyber Security, Crime, and Forensices of Wireless Networks and Applications, 9 (2016), 3833-3843.  doi: 10.1002/sec.1522.  Google Scholar

[25]

Y. Liang, Z. Cai, J. Yu, Q. Han and Y. Li, Deep learning based inference of private information using embedded sensors in smart devices, IEEE Network Magazine, 32 (2018), 8-14.  doi: 10.1109/MNET.2018.1700349.  Google Scholar

[26]

Y. Liang, Z. Cai, Q. Han and Y. Li, Location privacy leakage through sensory data, Security and Communication Networks, 2017 (2017), Article ID 7576307, 12 pages. doi: 10.1155/2017/7576307.  Google Scholar

[27]

L. Luu, R. Saha, I. Parameshwaran, P. Saxena and A. Hobor, On power splitting games in distributed computation: The case of bitcoin pooled mining, in IEEE 28th Computer Security Foundations Symposium, CSF 2015, Verona, Italy, 13-17 July, 2015, 2015, 397–411. doi: 10.1109/CSF.2015.34.  Google Scholar

[28]

L. Luu, Y. Velner, J. Teutsch and P. Saxena, Smart pool: Practical decentralized pooled mining, in 26th USENIX Security Symposium, USENIX Security 2017, Vancouver, BC, Canada, August 16-18, 2017., 2017, 1409–1426. [Online]. Available: https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/luu Google Scholar

[29]

R. Meni, A short note about variance and pool payouts, [Online], 2011, Available: https://bitcointalk.org/index.php?topic=5264.0 Google Scholar

[30]

C. Mooney and S. Mufson, Why the bitcoin craze is using up so much energy, 2018, [Online]. Available: https://www.washingtonpost.com/news/energy-environment/wp/2017/12/19/why-the-bitcoin-craze-is-using-up-so-much-energy/?utm_term=.2ec154762efa Google Scholar

[31]

S. Nakamoto, Bitcoin: A peer-to-peer electronic cash system, [Online], 2009, Available: http://www.bitcoin.org/bitcoin.pdf Google Scholar

[32]

K. Nayak, S. Kumar, A. Miller and E. Shi, Stubborn mining: Generalizing selfish mining and combining with an eclipse attack, in IEEE European Symposium on Security and Privacy, EuroS & P 2016, Saarbrücken, Germany, March 21-24, 2016, 2016, 305–320. doi: 10.1109/EuroSP.2016.32.  Google Scholar

[33]

M. Rosenfeld, Analysis of bitcoin pooled mining reward systems, CoRR, vol. abs/1112.4980, 2011. [Online]. Available: arXiv: 1112.4980 Google Scholar

[34]

M. Rosenfeld, Mining pools reward methods, presentation at bitcoin 2013 conference, [Online]. Available: http://www.youtube.com/watch?v=5sgdD4mGPfg Google Scholar

[35]

M. Rosenfeld, Double geometric method: Hopping-proof, low-variance reward system, [Online], Available: https://bitcointalk.org/index.php?topic=39497.0 Google Scholar

[36]

A. Sapirshtein, Y. Sompolinsky and A. Zohar, Optimal selfish mining strategies in bitcoin, in Financial Cryptography and Data Security - 20th International Conference, FC 2016, Christ Church, Barbados, February 22-26, 2016, Revised Selected Papers, 9603 (2017), 515–532. doi: 10.1007/978-3-662-54970-4\_30.  Google Scholar

[37]

O. Schrijvers, J. Bonneau, D. Boneh and T. Roughgarden, Incentive compatibility of bitcoin mining pool reward functions, in Financial Cryptography and Data Security - 20th International Conference, FC 2016, Christ Church, Barbados, February 22-26, 2016, Revised Selected Papers, 9603 (2017), 477–498. doi: 10.1007/978-3-662-54970-4\_28.  Google Scholar

[38]

B. L. Shultz and D. Bayer, Certification of witness: Mitigating blockchain fork attacks, Undergraduate Thesis in Mathematics, Columbia University in the City of New York, 2015. Google Scholar

[39]

S. Solat and M. Potop-Butucaru, Brief announcement: Zeroblock: Timestamp-free prevention of block-withholding attack in bitcoin, in Stabilization, Safety, and Security of Distributed Systems - 19th International Symposium, SSS 2017, Boston, MA, USA, November 5-8, 2017, Proceedings, 2017, 356–360. doi: 10.1007/978-3-319-69084-1_25.  Google Scholar

[40]

E. Swanson, Bitcoin mining calculator, 2013, [Online], Available: https://alloscomp.com/bitcoin/calculator/ Google Scholar

[41]

D. K. Tosh, S. Shetty, X. Liang, C. A. Kamhoua, K. A. Kwiat and L. Njilla, Security implications of blockchain cloud with analysis of block withholding attack, in 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID, 2017, 458–467. doi: 10.1109/CCGRID.2017.111.  Google Scholar

[42]

Y. Velner, J. Teutsch and L. Luu, Smart contracts make bitcoin mining pools vulnerable, in Financial Cryptography and Data Security - FC 2017 International Workshops, WAHC, BITCOIN, VOTING, WTSC, and TA, Sliema, Malta, April 7, 2017, Revised Selected Papers, 2017, 298–316. doi: 10.1007/978-3-319-70278-0_19.  Google Scholar

[43]

J. Wang, Z. Cai, Y. Li, D. Yang, J. Li and H. Gao, Protecting query privacy with differentially private k-anonymity in location-based services, Personal and Ubiquitous Computing, 2018, 1–17. Google Scholar

[44]

G. Wood, Ethereum: A secure decentralised generalised transaction ledger, Ethereum Project Yellow Paper, 151 (2014), 1-32.   Google Scholar

[45]

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Figure 1.  Proportional Mechanism
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Figure 2.  An example of PPLNS Mechanism
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Figure 3.  Scenarios of Selfish Mining
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Figure 4.  BWH attacker scenario: one attacker
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Figure 5.  BWH attacker scenario: two attackers
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Figure 6.  Bribe-style BWH attack scenario
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Figure 7.  Sponsored BWH Scenario
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Figure 8.  Race Competition
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Figure 9.  Attacker's dilemma
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Table 1.  Reward Mechanism Statistics [53]
Pool Name Reward Mechanism PPS fee Other fee
Antpool PPLNS & PPS 2.5% 0%
BTC.com FPPS 0% 4%
Slush Pool Score 2%
BTCC Pool PPS 2% 0%
P2Pool PPLNS 0%
BTCDig DGM 0%
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Pool Name Reward Mechanism PPS fee Other fee
Antpool PPLNS & PPS 2.5% 0%
BTC.com FPPS 0% 4%
Slush Pool Score 2%
BTCC Pool PPS 2% 0%
P2Pool PPLNS 0%
BTCDig DGM 0%
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