{"id":11609,"date":"2026-09-06T15:25:49","date_gmt":"2026-09-06T07:25:49","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=11609"},"modified":"2026-09-06T16:54:48","modified_gmt":"2026-09-06T08:54:48","slug":"jase-202612-35-018","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-018","title":{"rendered":"A Trusted Computing Framework for Intelligent Employment Recommendation of Industrial Talents Integrating Differential Privacy"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Xiaojuan Liang, Chunjie Xie, and Wenting Qiu<\/i><\/a><\/p>\n\n\n\n

Minxi Vocational and Technical College, Longyan, 364000, China<\/p>\n<\/div>\n\n\n\n

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Received: July 20, 2026
Accepted: August 13, 2026
Publication Date: September 06, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Overall Architecture of the DP-TCERS Trusted Job Recommendation System. <\/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: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.018<\/a> <\/p>\n\n\n\n

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

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

Keywords: industrial talent recommendation, differential privacy, trusted computing, privacy-preserving recommendation, scalable information systems, and recommendation security.<\/em><\/p>\n\n\n\n

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