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Differential Privacy-Based Permissioned Blockchain for Private Data Sharing in Industrial IoT

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Broadband Communications, Networks, and Systems (BROADNETS 2021)

Abstract

Permissioned blockchain such as Hyperledger fabric enables a secure supply chain model in Industrial Internet of Things (IIoT) through multichannel and private data collection mechanisms. However, the existing data sharing and querying mechanism in Hyperledger fabric is not suitable for supply chain environment in IIoT because the queries are evaluated on actual data stored on ledger which consists of sensitive information such as business secrets, and special discounts offered to retailers and individuals. To solve this problem, we propose a differential privacy-based permissioned blockchain using Hyperledger fabric to enable private data sharing in supply chain in IIoT (DH-IIoT). We integrate differential privacy into the chaindcode (smart contract) of Hyperledger fabric to achieve privacy preservation. As a result, the query response consists of perturbed data which protects the sensitive information in the ledger. We evaluate and compare our differential privacy integrated chaincode of Hyperledger fabric with the default chaincode setting of Hyperledger fabric for supply chain scenario. The results confirm that the proposed work maintains 96.15% of accuracy in the shared data while guarantees the protection of sensitive ledger’s data.

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References

  1. Ali, M.S., Vecchio, M., Pincheira, M., Dolui, K., Antonelli, F., Rehmani, M.H.: Applications of blockchains in the Internet of Things: a comprehensive survey. IEEE Commun. Surv. Tutor. 21(2), 1676–1717 (2019)

    Article  Google Scholar 

  2. Wen, Q., Gao, Y., Chen, Z., Wu, D.: A blockchain-based data sharing scheme in the supply chain by IIoT. In: IEEE International Conference on Industrial Cyber Physical Systems (ICPS), pp. 695–700. IEEE (2019)

    Google Scholar 

  3. Xiao, Y., Zhang, N., Lou, W., Hou, Y.T.: A survey of distributed consensus protocols for blockchain networks. IEEE Commun. Surv. Tutor. 22(2), 1432–1465 (2020)

    Article  Google Scholar 

  4. Hyperledger fabric documentation. https://hyperledger-fabric.readthedocs.io/en/release-2.2/. Accessed 26 Mar 2021

  5. Yasusaka, Y., Watanabe, C., Kitagawa, H.: Privacy-preserving pre-consensus protocol for blockchains. In: IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 1–8 (2019)

    Google Scholar 

  6. Wan, J., Li, J., Imran, M., Li, D.: A blockchain-based solution for enhancing security and privacy in smart factory. IEEE Trans. Ind. Inf. 15(6), 3652–3660 (2019)

    Article  Google Scholar 

  7. Liu, X., Yuan, D., Zhang, G., Chen, J., Yang, Y.: SwinDeW-C: a peer-to-peer based cloud workflow system. In: Furht, B., Escalante, A. (eds.) Handbook of Cloud Computing, pp. 309–332. Springer, Boston (2010). https://doi.org/10.1007/978-1-4419-6524-0_13

    Chapter  Google Scholar 

  8. Qi, L., Dou, W., Zhang, X., Chen, J.: A QoS-aware composition method supporting cross-platform service invocation in cloud environment. J. Comput. Syst. Sci. 78(5), 1316–1329 (2012)

    Article  Google Scholar 

  9. Song, X., Dou, W., Chen, J.: A workflow framework for intelligent service composition. Future Gener. Comput. Syst. 27(5), 627–636 (2011)

    Article  Google Scholar 

  10. Wang, L., Jie, W., Chen, J.: Grid Computing: Infrastructure, Service, and Applications. CRC Press, p. 528 (2009). ISBN-13: 978-1420067668

    Google Scholar 

  11. Chen, J., Yang, Y.: Temporal dependency based checkpoint selection for dynamic verification of fixed-time constraints in grid workflow systems. In: ACM/IEEE 30th International Conference on Software Engineering, pp. 141–150. IEEE (2008)

    Google Scholar 

  12. Puthal, D., Nepal, S., Ranjan, R., Chen, J.: DLSeF: a dynamic key-length-based efficient real-time security verification model for big data stream. ACM Trans. Embed. Comput. Syst. 16(2), 1–24 (2016)

    Google Scholar 

  13. Huang, J., Kong, L., Chen, G., Wu, M., Liu, X., Zeng, P.: Towards secure industrial IoT: blockchain system with credit-based consensus mechanism. IEEE Trans. Ind. Inf. 15(6), 3680–3689 (2019)

    Article  Google Scholar 

  14. Wang, E.K., Liang, Z., Chen, C.M., Kumari, S., Khan, M.K.: PoRX: a reputation incentive scheme for blockchain consensus of IIoT. Future Gener. Comput. Syst. 102, 140–151 (2020)

    Article  Google Scholar 

  15. Monero. https://www.getmonero.org/get-started/what-is-monero/. Accessed 18 Nov 2020

  16. Assaqty, M.I.S., et al.: Private-blockchain-based industrial IoT for material and product tracking in smart manufacturing. IEEE Netw. 34(5), 91–97 (2020)

    Article  Google Scholar 

  17. Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) Automata, Languages and Programming, pp. 1–12. Springer, Heidelberg (2006)

    Google Scholar 

  18. Zhu, T., Li, G., Zhou, W., Philip, S.Y.: Differentially private data publishing and analysis: a survey. IEEE Trans. Knowl. Data Eng. 29(8), 1619–1638 (2017)

    Article  Google Scholar 

  19. Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3–4), 211–407 (2014)

    MathSciNet  MATH  Google Scholar 

  20. Hyperledger caliper. https://www.hyperledger.org/use/caliper. Accessed 26 Mar 2021

  21. Xiao, X., Bender, G., Hay, M., Gehrke, J.: iReduct: differential privacy with reduced relative errors. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, pp. 229–240. Association for Computing Machinery, New York (2011). https://doi.org/10.1145/1989323.1989348

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Islam, M., Rehmani, M.H., Chen, J. (2022). Differential Privacy-Based Permissioned Blockchain for Private Data Sharing in Industrial IoT. In: Xiang, W., Han, F., Phan, T.K. (eds) Broadband Communications, Networks, and Systems. BROADNETS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-030-93479-8_5

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  • DOI: https://doi.org/10.1007/978-3-030-93479-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93478-1

  • Online ISBN: 978-3-030-93479-8

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