Abstract
The smart contract is a self-executing code that is managed by blockchain nodes, providing coordination and enforcement framework for agreements between network participants. However, smart contracts are not particularly “smart” since the virtual machine (VM) does not support the running environment of machine learning. To make the smart contract smarter, we propose a decentralized blockchain oracle framework to support smart contract training the machine learning model. In view of malicious nodes which may attack the process of training, we propose a consensus algorithm to prevent the malicious attack from malicious nodes. At the end of this paper, we do an experiment on two datasets: MNIST and CIFAR10. The result shows that our framework can prevent malicious attack efficiently and keep high accuracy. With our proposed framework, smart contracts have an ability to train or call machine learning model, making a smart contracts “smarter”.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2008). https://bitcoin.org/bitcoin.pdf
Dinh, T.T.A., Liu, R., Zhang, M., Chen, G., Ooi, B.C., Wang, J.: Untangling blockchain: a data processing view of blockchain systems. IEEE Trans. Knowl. Data Eng. 30, 1366–1385 (2018)
Yeow, K., Gani, A., Ahmad, R.W., Rodrigues, J.J.P.C., Ko, K.: Decentralized consensus for edge-centric internet of things: a review, taxonomy, and research issues, pp. 1513–1524, June 2017
Castro, M., Liskov, B.: Practical Byzantine fault tolerance, pp. 173–186 (1999)
Ongaro, D., Ousterhout, J.: In search of an understandable consensus algorithm. In: 2014 USENIX Annual Technical Conference (USENIXATC 2014), pp. 305–319 (2014)
Divyakant, M.: Issues and challenges with blockchain: a survey. Int. J. Comput. Sci. Eng. 6, 488–491 (2018)
Chen, X., Ji, J., Luo, C., Liao, W., Pan, L.: When machine learning meets blockchain: a decentralized, privacy-preserving and secure design. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 1178–1187 (2018)
Rana, S.S., Vaidya, N.H.: iPath: intelligent and optimal path selection for Byzantine fault tolerant communication. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 1095–1103 (2015)
Pawlak, M., Poniszewska-Marańda, A., Guziur, J.: Intelligent agents in a blockchain-based electronic voting system. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A.J. (eds.) IDEAL 2018. LNCS, vol. 11314, pp. 586–593. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03493-1_61
Li, K., Li, H., Hou, H., Li, K., Chen, Y.: Proof of vote: a high-performance consensus protocol based on vote mechanism & consortium blockchain. In: 2017 IEEE 19th International Conference on High Performance Computing and Communications, pp. 466–473 (2017)
Bravo-Marquez, F., Reeves, S., Ugarte, M.: Proof-of-learning: a blockchain consensus mechanism based on machine learning competitions. In: 2019 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPCON), pp. 119–124 (2019)
Xia, Q., Tao, Z., Hao, Z., Li, Q.: FABA: an algorithm for fast aggregation against byzantine attacks in distributed neural networks. In: IJCAI 2019, pp. 4824–4830 (2019)
Beniiche, A.: A Study of Blockchain Oracles. arXiv 2004.07140 (2020)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 62077015, No. U1811263 and No. 61772211), the Key-Area Research and Development Program of Guangdong Province, China (No. 2018B010109002) and the Science and Technology Project of Guangzhou Municipality, China (No. 201904010393), as well as the Guangzhou Key Laboratory of Big Data and Intelligent Education (No. 201905010009).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, S., Zhu, J., Lin, Z., Huang, J., Tang, Y. (2021). How to Make Smart Contract Smarter. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_54
Download citation
DOI: https://doi.org/10.1007/978-981-16-2540-4_54
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2539-8
Online ISBN: 978-981-16-2540-4
eBook Packages: Computer ScienceComputer Science (R0)
