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Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant Nos. 62072361, 62125205, U23A20303), Key Research and Development Program of Shaanxi (Grant No. 2022GY-019), Shaanxi Fundamental Science Research Project for Mathematics and Physics (Grant No. 22JSY019), Opening Project of Intelligent Policing Key Laboratory of Sichuan Province (Grant No. ZNJW2023KFMS002), and Open Fund of Key Laboratory of Computing Power Network and Information Security (Grant No. 2023ZD020).
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Supporting information Appendixes A–C. The supporting information is available online at info.scichina.com and 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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Miao, Y., Kuang, D., Li, X. et al. Efficient privacy-preserving federated learning under dishonest-majority setting. Sci. China Inf. Sci. 67, 159102 (2024). https://doi.org/10.1007/s11432-023-3977-9
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DOI: https://doi.org/10.1007/s11432-023-3977-9