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ACCEL: an efficient and privacy-preserving federated logistic regression scheme over vertically partitioned data

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Acknowledgements

This work was supported by National Key R&D Program of China (Grant No. 2021YFB3101300), National Natural Science Foundation of China (Grant Nos. 61972304, 61932015), and Science Foundation of the Ministry of Education (Grant No. MCM20200101).

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Authors and Affiliations

  1. State Key Laboratory of Integrated Networks Services, Xidian University, Xi’an, 710071, China

    Jiaqi Zhao, Hui Zhu, Fengwei Wang & Hui Li

  2. Faculty of Computer Science, University of New Brunswick, Fredericton, E3B 5A3, Canada

    Rongxing Lu

  3. China Mobile (Suzhou) Software Technology Co., Ltd., Suzhou, 215153, China

    Zhongmin Zhou & Haitao Wan

Authors
  1. Jiaqi Zhao
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  2. Hui Zhu
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  3. Fengwei Wang
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  4. Rongxing Lu
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  5. Hui Li
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  6. Zhongmin Zhou
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  7. Haitao Wan
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Corresponding author

Correspondence to Hui Zhu.

Additional information

Supporting information Appendix A. The supporting information is available online at https://info.scichina.com and https://link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Zhao, J., Zhu, H., Wang, F. et al. ACCEL: an efficient and privacy-preserving federated logistic regression scheme over vertically partitioned data. Sci. China Inf. Sci. 65, 170307 (2022). https://doi.org/10.1007/s11432-021-3415-1

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  • DOI: https://doi.org/10.1007/s11432-021-3415-1

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