MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural Network Training Based on Functional Encryption  

作　　者：Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang 

机构地区：[1]The State Key Laboratory of Public Big Data,College of Computer Science and Technology,Guizhou University,Guiyang,550025,China [2]Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Shandong Computer Science Center,Qilu University of Technology(Shandong Academy of Sciences),Jinan,250014,China [3]China Industrial Control Systems Cyber Emergency Response Team,Beijing,100040,China

出　　处：《Computers, Materials & Continua》2025年第3期5387-5405,共19页计算机、材料和连续体(英文)

基　　金：supported by Natural Science Foundation of China(Nos.62303126,62362008,author Z.Z,https://www.nsfc.gov.cn/,accessed on 20 December 2024);Major Scientific and Technological Special Project of Guizhou Province([2024]014);Guizhou Provincial Science and Technology Projects(No.ZK[2022]General149) ,author Z.Z,https://kjt.guizhou.gov.cn/,accessed on 20 December 2024);The Open Project of the Key Laboratory of Computing Power Network and Information Security,Ministry of Education under Grant 2023ZD037,author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024);Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2024B25),author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024).

摘　　要：Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关 键 词：Functional encryption multi-sourced heterogeneous data privacy preservation neural networks 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]