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Data privacy-preserving distributed knowledge discovery based on the blockchain

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Abstract

Data are collected and regarded as valuable assets in many business domains. Their owner would not want to disclose them to the public due to their potential value. Distributed knowledge discovery techniques have been proposed which assume the cooperation of data owners even though they might not behave in a trustworthy manner. When a party decides to quit the cooperation in the distributed knowledge discovery, the other parties cannot continue the discovery task and hence they get some disadvantage due to the party’s betrayal. This paper is concerned with data privacy-preserving distributed knowledge discovery which gives penalty to the party who quits the cooperation in the discovery process. It proposes a blockchain-based distributed machine learning method which does not disclose the participating parties’ data and gives the penalty to betraying parties. The proposed method makes the participating parties communicate with each other via the smart contract on the blockchain network. It uses a blockchain-based incentive system to establish trust among parties and to improve the quality of discovery knowledge. The proposed method has been implemented with a smart contract on the blockchain and tested for a benchmark data.

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References

  1. Baby V, Subhash Chandra N (2016) Privacy-preserving distributed data mining technique: a survey. Int J Comput Appl 143(10):37–41

    Google Scholar 

  2. Pardo TA, Cresswell AM, Tompson F, Zhang J (2006) Knowledge sharing in cross-boundary information system development in the public sector. Inf Technol Manag 7(4):293–313

    Article  Google Scholar 

  3. Chung K, Boutaba R, Hariri S (2016) Knowledge based decision support system. Inf Technol Manag 17(1):1–3

    Article  Google Scholar 

  4. Bhati BS, Venkataram P (2017) Preserving data privacy during data transfer in MANETs. Wirel Personal Commun 97(3):4063–4086

    Article  Google Scholar 

  5. Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system

  6. Han K (2017) Bitcoin as an alternative investment vehicle. Inf Technol Manag 18(4):265–275

    Article  Google Scholar 

  7. Wood G (2014) Ethereum: a secure decentralised generalised transaction ledger. Ethereum Proj Yellow Pap 151:1–32

    Google Scholar 

  8. Dannen C (2017) Introducing ethereum and solidity. Apress, Berkeley

    Book  Google Scholar 

  9. Biryukov A, Pustogarov I (2015) Proof-of-work as anonymous micropayment: rewarding a Tor relay. In: Brenner M, Christin N, Johnson B, Rohloff K (eds) International conference on financial cryptography and data. Springer, Berlin, pp 445–455

    Chapter  Google Scholar 

  10. Bahga A, Madisetti V (2017) Blockchain applications: a hands-on approach. In: VPT

  11. Swan M (2015) Blockchain thinking: the brain as a DAC (decentralized autonomous organization). In: Texas Bitcoin conference. Chicago, pp 27–29

  12. Bailis P, Narayanan A, Miller A, Han S (2017) Research for practice: cryptocurrencies, blockchains, and smart contracts; hardware for deep learning. Commun ACM 60(5):48–51

    Article  Google Scholar 

  13. Harnsamut N, Natwichai J (2008) A novel heuristic algorithm for privacy preserving of associative classification. In: Proceedings of Pacific Rim international conference on artificial intelligence. Springer, Berlin, pp 273–283

  14. Chhatrapati MR, Sherasiya S (2015) Privacy preserving data mining using heuristic approach. Int J Innov Res Sci Technol 1(10):2349–6010

    Google Scholar 

  15. Andruszkiewicz P (2016) Privacy preserving reconstruction-based techniques and randomisation-based methods for calculating surveys’ statistics and participants sampling in deliberative consultations. In: Proceedings of the tenth international conference on digital society and eGovernments

  16. Pathak K, Chaudhari NS, Tiwari A (2012) Privacy-preserving data sharing using data reconstruction based approach. In: IJCA Special Issue on Communication Security, pp 64–68

  17. Pinkas B (2002) Cryptographic techniques for privacy-preserving data mining. ACM SIGKDD Explor Newsl 4(2):12–19

    Article  Google Scholar 

  18. Shah A, Gulati R (2016) Privacy preserving data mining: techniques classification and implications—a survey. Int J Comput Appl 137(12):40–46

    Google Scholar 

  19. Mendis GJ, Sabounchi M, Wei J, Roche R (2018) Blockchain as a Service: an autonomous, privacy preserving, decentralized architecture for deep learning. arXiv preprint arXiv:1807.02515

  20. Kurtulmus AB, Daniel K (2018) Trustless machine learning contracts; evaluating and exchanging machine learning models on the Ethereum blockchain. arXiv preprint arXiv:1802.10185

  21. Abadi M (2016) TensorFlow: learning functions at scale. ACM SIGPLAN Notices

Download references

Acknowledgements

This work was supported by the Next-Generation Information Computing Development Program through the National Research Foundation of Korea, Republic of Korea (Grant No. NRF-2017M3C4A7069432).

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Authors and Affiliations

  1. Department of Computer Science, Chungbuk National University, Cheongju, Korea

    Keon Myung Lee

  2. Department of Computer Science and Engineering, University of Colarado Denver, Denver, USA

    Ilkyeun Ra

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  1. Keon Myung Lee
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  2. Ilkyeun Ra
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Correspondence to Keon Myung Lee.

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Lee, K.M., Ra, I. Data privacy-preserving distributed knowledge discovery based on the blockchain. Inf Technol Manag 21, 191–204 (2020). https://doi.org/10.1007/s10799-020-00317-1

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  • DOI: https://doi.org/10.1007/s10799-020-00317-1

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