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To realize collaborative computation among ICVs, federated learning (FL) or federated\u2010based large language model (FedLLM) as a promising distributed approach has been used to support various collaborative application computations in ICVs scenarios, for example, analyzing vehicle driving information to realize trajectory prediction, voice\u2010activated controls, conversational AI assistants. Unfortunately, recent research reveals that FL systems are still faced with privacy challenges from honest\u2010but\u2010curious server, honest\u2010but\u2010curious distributed participants, or the collusion between participants and the server. These threats can lead to the leakage of sensitive private data, such as location information and driving conditions. Homomorphic encryption (HE) is one of the typical mitigation that has few effects on the model accuracy and has been studied before. However, single\u2010key HE cannot resist collusion between participants and the server, multikey HE is not suitable for ICVs scenarios. In this work, we proposed a novel approach that combines FL with homomorphic proxy re\u2010encryption (PRE) which is based on participants\u2019 ID information. By doing so, the FL\u2010based ICVs can be able to successfully defend against privacy threats. In addition, we analyze the security and performance of our method, and the theoretical analysis and the experiment results show that our defense framework with ID\u2010based homomorphic PRE can achieve a high\u2010security level and efficient computation. We anticipate that our approach can serve as a fundamental point to support the extensive research on FedLLMs privacy\u2010preserving.<\/jats:p>","DOI":"10.1049\/ise2\/4632786","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T06:12:05Z","timestamp":1741759925000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Using Homomorphic Proxy Re\u2010Encryption to Enhance Security and Privacy of Federated Learning\u2010Based Intelligent Connected Vehicles"],"prefix":"10.1049","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2475-4232","authenticated-orcid":false,"given":"Yang","family":"Bai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9476-7158","authenticated-orcid":false,"given":"Yutang","family":"Rao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9073-7078","authenticated-orcid":false,"given":"Hongyan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0868-6648","authenticated-orcid":false,"given":"Wentao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7040-8919","authenticated-orcid":false,"given":"Gaojie","family":"Xing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8347-2210","authenticated-orcid":false,"given":"Jiawei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoshu","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"e_1_2_14_1_2","first-page":"1","article-title":"Palm: Scaling Language Modeling With Pathways","volume":"24","author":"Chowdhery A.","year":"2023","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_14_2_2","unstructured":"ChenC. 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