Journal of Applied Science and Engineering

Published by Tamkang University Press

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A Trusted Computing Framework for Intelligent Employment Recommendation of Industrial Talents Integrating Differential Privacy

 2026

 Volume 35

Xiaojuan Liang, Chunjie Xie, and Wenting Qiu

Minxi Vocational and Technical College, Longyan, 364000, China

Received: July 20, 2026
Accepted: August 13, 2026
Publication Date: September 06, 2026

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Overall Architecture of the DP-TCERS Trusted Job Recommendation System.

 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.202612_35.018

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Intelligent employment recommendation for industrial talent must process multi-source data profiles, certificates, behavior logs, and company sources yet existing methods emphasize accuracy over privacy and high-concurrency deployment. This study designs DP-TCERS, a differential-privacy trusted computing framework: it perturbs user/job features via local differential privacy and gradient noise against inference attacks, builds attention-fused towers for matching, scores trustworthiness from consistency and reliability, and lowers overhead via caching and parallel inference. Simulations show precision@10/recall@10/F1@10/NDCG@10 of 0.773/0.732/0.752/0.765 below non-DP matching but above CF, NCF, DeepFM – with attack success rates falling to 0.198-0.231 and latency at 10,000 users reaching 294 ms versus 1,486 ms single-node.

Keywords: industrial talent recommendation, differential privacy, trusted computing, privacy-preserving recommendation, scalable information systems, and recommendation security.

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