{"id":8669,"date":"2026-06-27T14:32:50","date_gmt":"2026-06-27T06:32:50","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=8669"},"modified":"2026-06-27T16:28:47","modified_gmt":"2026-06-27T08:28:47","slug":"jase-202610-33-017","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-017","title":{"rendered":"Simulation Research on Secure Sharing of Regional Ecommerce AI Models Based on Sensor Data De-sensitization"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Changqian Wu1<\/sup> and Lili Zheng2<\/sup><\/i><\/a><\/p>\n\n\n\n

1<\/sup>College of Finance and Commerce, Minxi Vocational & Technical College, Longyan 364021, Fujian, China.<\/p>\n\n\n\n

2<\/sup>College of Information Technology and Engineering, Ningde Vocational and Technical College, Ningde 355099, Fujian, China. <\/p>\n<\/div>\n\n\n\n

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Received: April 6, 2026
Accepted: May 15, 2026
Publication Date: June 27, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Privacy-Preserving Data Preparation for Regional E-commerce IoT Data<\/p>\n<\/div>\n<\/div>\n\n\n\n

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

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.017<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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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.<\/p>\n\n\n\n

Keywords:  LSTM, Attention Mechanism, E-commerce Sales Forecasting, Regional Data, Sensor Data De-sensitization, Prediction Accuracy<\/em><\/p>\n\n\n\n

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