TY  - EJOU
AU  - Li, Hao 
AU  - Shao, Kuan 
AU  - Wang, Xin 
AU  - Wang, Mufeng 
AU  - Zhang, Zhenyong 

TI  - MMH-FE: A Multi-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural Network Training Based on Functional Encryption
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 82
IS  - 3
SN  - 1546-2226

AB  - 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 a multi-precision, multi-sourced, and multi-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  to . Additionally, it offers a memory space optimization of  times compared to the previous best approach.
KW  - Functional encryption; multi-sourced heterogeneous data; privacy preservation; neural networks

DO  - 10.32604/cmc.2025.059718