ePMLF: Efficient and Privacy-Preserving Machine Learning Framework Based on Fog Computing

doi:10.1155/2023/8292559

ePMLF: Efficient and Privacy-Preserving Machine Learning Framework Based on Fog Computing

2023-02-27

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DOI：10.1155/2023/8292559

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With the continuous improvement of computation and communication capabilities, the Internet of Things (IoT) plays a vital role in many intelligent applications. Therefore, IoT devices generate a large amount of data every day, which lays a solid foundation for the success of machine learning. However, the strong privacy requirements of the IoT data make its machine learning very difficult. To protect data privacy, many privacy-preserving machine learning schemes have been proposed. At present, most schemes only aim at specific models and lack general solutions, which is not an ideal solution in engineering practice. In order to meet this challenge, we propose an efficient and privacy-preserving machine learning training framework (ePMLF) in a fog computing environment. The ePMLF framework can let the software service provider (SSP) perform privacy-preserving model training with the data on the fog nodes. The security of the data on the fog nodes can be protected and the model parameters can only be obtained by SSP. The proposed secure data normalization method in the framework further improves the accuracy of the training model. Experimental analysis shows that our framework significantly reduces the computation and communication overhead compared with the existing scheme.

Keywords:

SECURE

AGGREGATION

SCHEME

ePMLF: Efficient and Privacy-Preserving Machine Learning Framework Based on Fog Computing

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