Advertisement

The Game Among Bribers in a Smart Contract System

  • Lin ChenEmail author
  • Lei XuEmail author
  • Zhimin GaoEmail author
  • Nolan ShahEmail author
  • Ton Chanh LeEmail author
  • Yang LuEmail author
  • Weidong ShiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10958)

Abstract

Blockchain has been used to build various applications, and the introduction of smart contracts further extends its impacts. Most of existing works consider the positive usage of smart contracts but ignore the other side of it: smart contracts can be used in a destructive way, particularly, they can be utilized to carry out bribery. The hardness of tracing a briber in a blockchain system may even motivate bribers. Furthermore, an adversary can utilize bribery smart contracts to influence the execution results of other smart contracts in the same system. To better understand this threat, we propose a formal framework to analyze bribery in the smart contract system using game theory. We give a full characterization on how the bribery budget of a briber may influence the execution of a smart contract if the briber tries to manipulate its execution result by bribing users in the system.

References

  1. 1.
    Bredereck, R., Chen, J., Faliszewski, P., Nichterlein, A., Niedermeier, R.: Prices matter for the parameterized complexity of shift bribery. Inf. Comput. 251, 140–164 (2016)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bredereck, R., Faliszewski, P., Niedermeier, R., Talmon, N.: Complexity of shift bribery in committee elections. In: AAAI, pp. 2452–2458 (2016)Google Scholar
  3. 3.
    Bredereck, R., Faliszewski, P., Niedermeier, R., Talmon, N.: Large-scale election campaigns: combinatorial shift bribery. J. Artif. Intell. Res. 55, 603–652 (2016)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Buterin, V.: A next-generation smart contract and decentralized application platform. white paper (2014)Google Scholar
  5. 5.
    Chen, L., Xu, L., Gao, Z., Shah, N., Lu, Y., Shi, W.: Smart contract execution-the (+-)-biased ballot problem. In: LIPIcs-Leibniz International Proceedings in Informatics. vol. 92. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2017)Google Scholar
  6. 6.
    Chen, L., Xu, L., Shah, N., Gao, Z., Lu, Y., Shi, W.: Decentralized execution of smart contracts: agent model perspective and its implications. In: Brenner, M., et al. (eds.) FC 2017. LNCS, vol. 10323, pp. 468–477. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-70278-0_29CrossRefGoogle Scholar
  7. 7.
    Chen, L., et al.: Protecting election from bribery: new approach and computational complexity characterization (extended abstract). In: Proceedings of the 2018 International Conference on Autonomous Agents and Multiagent Systems, vol. 1. International Foundation for Autonomous Agents and Multiagent Systems (2018)Google Scholar
  8. 8.
    Dey, P., Misra, N., Narahari, Y.: Frugal bribery in voting. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 2466–2472. AAAI Press (2016)Google Scholar
  9. 9.
    Dorn, B., Krüger, D.: On the hardness of bribery variants in voting with CP-nets. Ann. Math. Artif. Intell. 77(3–4), 251–279 (2016)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Dorn, B., Krüger, D., Scharpfenecker, P.: Often harder than in the constructive case: destructive bribery in CP-nets. In: Markakis, E., Schäfer, G. (eds.) WINE 2015. LNCS, vol. 9470, pp. 314–327. Springer, Heidelberg (2015).  https://doi.org/10.1007/978-3-662-48995-6_23CrossRefGoogle Scholar
  11. 11.
    Elkind, E., Faliszewski, P., Slinko, A.: Swap bribery. In: Mavronicolas, M., Papadopoulou, V.G. (eds.) SAGT 2009. LNCS, vol. 5814, pp. 299–310. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-04645-2_27CrossRefGoogle Scholar
  12. 12.
    Erdélyi, G., Reger, C., Yang, Y.: The complexity of bribery and control in group identification. In: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, pp. 1142–1150. International Foundation for Autonomous Agents and Multiagent Systems (2017)Google Scholar
  13. 13.
    Eyal, I., Sirer, E.G.: Majority is not enough: bitcoin mining is vulnerable. In: Christin, N., Safavi-Naini, R. (eds.) FC 2014. LNCS, vol. 8437, pp. 436–454. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-45472-5_28CrossRefGoogle Scholar
  14. 14.
    Faliszewski, P.: Nonuniform bribery. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 3, pp. 1569–1572. International Foundation for Autonomous Agents and Multiagent Systems (2008)Google Scholar
  15. 15.
    Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L.A.: How hard is bribery in elections? J. Artif. Intell. Res. 35, 485–532 (2009)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L.A., Rothe, J.: Llull and copeland voting computationally resist bribery and constructive control. J. Artif. Intell. Res. 35, 275–341 (2009)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Faliszewski, P., Rothe, J.: Control and Bribery in Voting. Cambridge University Press, Cambridge (2016)CrossRefGoogle Scholar
  18. 18.
    Kaczmarczyk, A., Faliszewski, P.: Algorithms for destructive shift bribery. In: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, pp. 305–313. International Foundation for Autonomous Agents and Multiagent Systems (2016)Google Scholar
  19. 19.
    Knop, D., Kouteckỳ, M., Mnich, M.: Voting and bribing in single-exponential time. In: LIPIcs-Leibniz International Proceedings in Informatics, vol. 66. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2017)Google Scholar
  20. 20.
    Kothapalli, A., Cordi, C.: A bribery framework using smartcontracts (2017)Google Scholar
  21. 21.
    Lewenberg, Y., Bachrach, Y., Sompolinsky, Y., Zohar, A., Rosenschein, J.S.: Bitcoin mining pools: a cooperative game theoretic analysis. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, pp. 919–927. International Foundation for Autonomous Agents and Multiagent Systems (2015)Google Scholar
  22. 22.
    Luu, L., Chu, D.H., Olickel, H., Saxena, P., Hobor, A.: Making smart contracts smarter. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 254–269. ACM (2016)Google Scholar
  23. 23.
    Mattei, N., Pini, M.S., Venable, K.B., Rossi, F.: Bribery in voting over combinatorial domains is easy. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, vol. 3, pp. 1407–1408. International Foundation for Autonomous Agents and Multiagent Systems (2012)Google Scholar
  24. 24.
    Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash system (2008)Google Scholar
  25. 25.
    Osborne, M.J., Rubinstein, A.: A Course in Game Theory. MIT Press, Cambridge (1994)zbMATHGoogle Scholar
  26. 26.
    Pini, M.S., Rossi, F., Venable, K.B.: Bribery in voting with soft constraints. In: AAAI (2013)Google Scholar
  27. 27.
    Sapirshtein, A., Sompolinsky, Y., Zohar, A.: Optimal selfish mining strategies in bitcoin. arXiv preprint arXiv:1507.06183 (2015)
  28. 28.
    Szabo, N.: Formalizing and securing relationships on public networks. First Monday 2(9) (1997)Google Scholar
  29. 29.
    Vazirani, V.V.: Approximation Algorithms. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-662-04565-7CrossRefGoogle Scholar
  30. 30.
    Vukolić, M.: The quest for scalable blockchain fabric: proof-of-work vs. BFT replication. In: Camenisch, J., Kesdoğan, D. (eds.) iNetSec 2015. LNCS, vol. 9591, pp. 112–125. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39028-4_9CrossRefGoogle Scholar
  31. 31.
    Xu, L., Chen, L., Gao, Z., Lu, Y., Shi, W.: CoC: secure supply chain management system based on public ledger. In: 2017 26th International Conference on Computer Communication and Networks (ICCCN), pp. 1–6. IEEE (2017)Google Scholar
  32. 32.
    Xu, L., Chen, L., Shah, N., Gao, Z., Lu, Y., Shi, W.: DL-BAC: distributed ledger based access control for web applications. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 1445–1450. International World Wide Web Conferences Steering Committee (2017)Google Scholar
  33. 33.
    Xu, L., et al.: Enabling the sharing economy: privacy respecting contract based on public blockchain. In: Proceedings of the ACM Workshop on Blockchain, Cryptocurrencies and Contracts, pp. 15–21. ACM (2017)Google Scholar
  34. 34.
    Yang, Y., Shrestha, Y.R., Guo, J.: How hard is bribery in party based elections? In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, pp. 1725–1726. International Foundation for Autonomous Agents and Multiagent Systems (2015)Google Scholar
  35. 35.
    Yang, Y., Shrestha, Y.R., Guo, J.: How hard is bribery with distance restrictions? In: ECAI, pp. 363–371 (2016)Google Scholar
  36. 36.
    Yin, Y., Vorobeychik, Y., An, B., Hazon, N.: Optimally protecting elections. In: IJCAI, pp. 538–545 (2016)Google Scholar

Copyright information

© International Financial Cryptography Association 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of HoustonHoustonUSA

Personalised recommendations