Abstract
Theoretically, cross-department predictive modeling can improve the operational efficiency of an enterprise, particularly on enterprise resource planning. For example, a model that predicts the volume of purchase goods will be more generalizable if the predication is based on the data from multiple departments. Most existing cross-department predictive models rely on a centralized technology, in which security and robustness are ignored, including unreliable single-point or malicious modification of records. Therefore, our works propose a decentralized framework to combine Blockchain technology with exited model so as to apply in predictive enterprise resource planning. In detail, model parameter estimation will be trained by without revealing any other information, which means only model-related data are exchanged across departments. In order to apply transaction metadata to disseminate models, we introduce neural networks combine with a private Blockchain network. In addition, we design an algorithm to train the neural networks that combine the loss function from each local model to achieve the smallest global level validation loss. Finally, we implement the experiments to prove the effectiveness of our framework by applying it to multi typical tasks in enterprise resource planning. Experimental results reveal the advantages of this framework on both tasks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wang, S., Jiang, X., Wu, Y., Cui, L., Cheng, S., Ohno-Machado, L.: Expectation propagation logistic regression (explorer): distributed privacy-preserving online model learning. J. Biomed. Inform. 46, 480–496 (2013)
Wu, Y., Jiang, X., Kim, J., Ohno-Machado, L.: Grid binary logistic regression (GLORE): building shared models without sharing data. J. Am. Med. Inform. Assoc. 19, 758–764 (2012)
Fromknecht, C., Velicanu, D., Yakoubov, D.: A decentralized public key infrastructure with identity retention. IACR Cryptology ePrint Archive, p. 803 (2014)
Luu, L., Narayanan, V., Zheng, K.B.C., Gilbert, S., Saxena, P.: SCP: a computationally-scalable byzantine consensus protocol for blockchains. Cryptology ePrint Archive Report, p. 1168 (2015)
Lamport, L., Shostak, R., Pease, M.: The byzantine generals problem. ACM Trans. Program. Lang. Syst 4, 382–401 (1982)
Bissias, G., Ozisik, A.P., Levine, B.N., Liberatore, M.: Sybil-resistant mixing for bitcoin. Proceedings of the 13th Workshop on Privacy in the Electronic Society, pp. 149–158 (2014)
Garay, J., Kiayias, A., Leonardos, N.: The bitcoin backbone protocol: analysis and applications. In: Oswald, E., Fischlin, M. (eds.) EUROCRYPT 2015, Part II. LNCS, vol. 9057, pp. 281–310. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46803-6_10
Jiang, W., et al.: WebGLORE: a web service for grid logistic regressions. Bioinformatics 29, 3238–3240 (2013)
Yan, F., Sundaram, S., Vishwanathan, S., Qi, Y.: Distributed autonomous online learning: regrets and intrinsic privacy preserving properties. IEEE Trans. Knowl. Data. Eng. 25, 2483–2493 (2013)
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_9
Mainelli, M., Smith, M.: Sharing ledgers for sharing economies: an exploration of mutual distributed ledgers (aka blockchain technology). J. Finance Perspect. 3, 38–69 (2015)
Buterin, V.: A next-generation smart contract and decentralized application platform. White Paper (2014)
Swan, M.: Blockchain: Blueprint for a New Economy. O’Reilly Media Inc., Sebastopol (2015)
Maesa, D.D.F., Mori, P.: Blockchain 3.0 applications survey. J. Parallel Distrib. Comput. 138, 99–114 (2020)
Irving, G., Holden, J.: How blockchain-timestamped protocols could improve the trustworthiness of medical science. F1000Research 5, 222 (2016)
McKernan, K.J.: The chloroplast genome hidden in plain sight, open access publishing and anti-fragile distributed data sources. Mitochondrial DNA 27, 1–2 (2015)
Jenkins, J., Kopf, J., Tran, B.Q., Frenchi, C., Szu, H.: Bio-mining for biomarkers with a multi-resolution block chain. In: Spie Sensing Technology + Applications, pp. 1–10 (2015)
Baxendale, G.: Can blockchain revolutionise EPRs? ITNOW 58, 38–39 (2016)
Witchey, N.J.: Healthcare transaction validation via blockchain proof-of-work, systems and methods. Patent 20150332283, A1 (2015)
Dubovitskaya, A., Xu, Z., Ryu, S., Schumacher, M., Wang, F.: Secure and trustable electronic medical records sharing using blockchain. In: AMIA Annual Symposium Proceedings, vol. 2017, p. 650. American Medical Informatics Association (2017)
Ekblaw, A., Azaria, A., Halamka, J.D., Lippman, A.: A case study for blockchain in healthcare: “medrec” prototype for electronic health records and medical research data. In: Proceedings of IEEE Open and Big Data Conference, vol. 13, p. 13 (2016)
Coelho, F.C.: Optimizing disease surveillance by reporting on the blockchain. bioRxiv, p. 278473 (2018)
Gem (2019). https://gem.co/health/. Accessed 30 Nov 2019
Healthcare working group (2019). https://www.hyperledger.org/industries/healthcare. Accessed 30 Nov 2019
Lai, S.W., Xu, L.H., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Proceedings of the 29th International Conference on Artificial Intelligence, pp. 2267–2273 (2015)
Buhmann, M.D.: Radial basis functions: theory and implementations. Cambridge University, Cambridge (2003)
Hyndman, J.B., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22, 679–688 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wu, Z., Qin, Y., Li, Y., Cheng, B., Lin, Z., Zhu, J. (2022). Decentralized Predictive Enterprise Resource Planning Framework on Private Blockchain Networks Using Neural Networks. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1491. Springer, Singapore. https://doi.org/10.1007/978-981-19-4546-5_1
Download citation
DOI: https://doi.org/10.1007/978-981-19-4546-5_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-4545-8
Online ISBN: 978-981-19-4546-5
eBook Packages: Computer ScienceComputer Science (R0)