Journal of Applied Science and Engineering

Published by Tamkang University Press

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Simulation Research on Secure Sharing of Regional Ecommerce AI Models Based on Sensor Data De-sensitization

 2026

 Volume 33

Changqian Wu1 and Lili Zheng2

1College of Finance and Commerce, Minxi Vocational & Technical College, Longyan 364021, Fujian, China.

2College of Information Technology and Engineering, Ningde Vocational and Technical College, Ningde 355099, Fujian, China.

Received: April 6, 2026
Accepted: May 15, 2026
Publication Date: June 27, 2026

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Privacy-Preserving Data Preparation for Regional E-commerce IoT Data

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation:  BibTeX | http://dx.doi.org/10.6180/jase.202610_33.017  

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Recent developments in e-commerce highlight the importance of AI models, particularly LSTM, for improving sales forecasting and capturing regional patterns. However, challenges remain in handling sensitive customer data, enabling secure cross-regional learning, and addressing regional variability. This study proposes a secure and scalable regional e-commerce forecasting framework integrating Differential Privacy (DP) and Secure Multi
Party Computation (SMPC). The proposed approach achieves strong performance (MAE=0.691, RMSE=0.951, MAPE=0.519, R2=0.9663), ensuring accurate, scalable, and privacy-preserving forecasting.

Keywords:  LSTM, Attention Mechanism, E-commerce Sales Forecasting, Regional Data, Sensor Data De-sensitization, Prediction Accuracy

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