Hao Li1,#, Kuan Shao1,#, Xin Wang2, Mufeng Wang3, Zhenyong Zhang1,2,*
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5387-5405, 2025, DOI:10.32604/cmc.2025.059718
- 06 March 2025
Abstract 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 More >
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