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MLChain: a privacy-preserving model learning framework using blockchain

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Abstract

In this work, we present a blockchain-based secure and flexible distributed privacy-preserving online model that helps in sharing key features of datasets across multiple organizations without violating the privacy of data. In our model, all members are encouraged to participate, discouraged to write fake data. Learning is carried out without sharing of raw data, and data sharing is immutable that improves prediction results of the data held by each member of an industry. We also propose a new consensus algorithm—Proof of Share for adding a valid transaction to the blockchain, thus preventing non participating members from reading any of the data shared by the peer and discouraging fake writes. We evaluated our model on 3, 5, and 10 members setup by applying decision tree, logistic regression, Gaussian naive Bayes, and support vector machine classifiers. The maximum increase of \(26.9231\%\) was observed in accuracy where results of a member’s data were taken as baseline. \(F_{\beta }(\beta =0.5)\) score increased by 0.4533 and \(F_{1}\) score by 0.0800. The proposed model to the best of our knowledge is the only one that encourages all members to participate, rather than being passive listeners and discourages a member from forging results thus rendering it suitable for utilization in domains like health care, finance, education, etc. where data are unevenly split and secrecy of data and peers is required.

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Data availability

Data are publicly available on UCI machine learning Repository https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State [37]

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Funding

The study has not been funded by any institute or agency.

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Authors and Affiliations

  1. Department of Information Technology, Indira Gandhi Delhi Technical University for Women, New Delhi, India

    Vidhi Bansal & Mohona Ghosh

  2. Department of Computer Engineering, National Institute of Technology Kurukshetra, Kurukshetra, India

    Niyati Baliyan

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  1. Vidhi Bansal
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Correspondence to Mohona Ghosh.

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Appendix

Appendix

1.1 Tables and figures of results on 3-node setup

See Figs. 12, 13, 14, 15. Tables 11, 12, 13, 14.

Fig. 13
figure 13

Performance of logistic regression on 3-node setup, a accuracy, b increase in accuracy, c \(F_{1}\) score, d \(F_{\beta }(\beta =0.5)\) score

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Fig. 14
figure 14

Performance of Gaussian naive Bayes on 3-node setup, a accuracy, b increase in accuracy, c \(F_{1}\) score, d \(F_{\beta }(\beta =0.5)\) score

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Fig. 15
figure 15

Support vector machine results on 3-node setup, a accuracy, b increase in accuracy, c \(F_{1}\) score, d \(F_{\beta }(\beta =0.5)\) score

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Table 12 Performance of Logistic Regression on 3 node setup
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Table 13 Performance of Gaussian naive Bayes on 3 node setup
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Table 14 Performance of support vector machine on 3 node setup
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1.2 Figures of results on 5-node setup with a subset of EEG dataset

See Figs. 16, 17, 18, 19.

Fig. 16
figure 16

Performance of decision tree on 5-node setup, a accuracy, b increase in accuracy, c \(F_{1}\) score, d \(F_{\beta }(\beta =0.5)\) score

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Fig. 17
figure 17

Performance of logistic regression on 5-node setup, a accuracy, b increase in accuracy, c \(F_{1}\) score d \(F_{\beta }(\beta =0.5)\) score

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Fig. 18
figure 18

Performance of Gaussian naive Bayes on 5-node setup, a accuracy, b increase in accuracy, c \(F_{1}\) score, d \(F_{\beta }(\beta =0.5)\) score

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Fig. 19
figure 19

Support vector machine results on 5-node setup, a accuracy, b increase in accuracy, c \(F_{1}\) score, d \(F_{\beta }(\beta =0.5)\) score

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Bansal, V., Baliyan, N. & Ghosh, M. MLChain: a privacy-preserving model learning framework using blockchain. Int. J. Inf. Secur. 23, 649–677 (2024). https://doi.org/10.1007/s10207-023-00754-3

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