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However, current VFL systems usually primarily focus on the privacy protection during model training, while the preparation of training data receives little attention. In real-world applications, like smart healthcare, the process of the training data preparation may involve some participant\u2019s intention which could be privacy information for this participant. To protect the privacy of the model training intention, we describe the idea of Intention-Hiding Vertical Federated Learning (IHVFL) and illustrate a framework to achieve this privacy-preserving goal. First, we construct two secure screening protocols to enhance the privacy protection in feature engineering. Second, we implement the work of sample alignment bases on a novel private set intersection protocol. Finally, we use the logistic regression algorithm to demonstrate the process of IHVFL. Experiments show that our model can perform better efficiency (less than 5min) and accuracy (97%) on Breast Cancer medical dataset while maintaining the intention-hiding goal.<\/jats:p>","DOI":"10.1186\/s42400-023-00166-9","type":"journal-article","created":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T04:01:36Z","timestamp":1696392096000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["IHVFL: a privacy-enhanced intention-hiding vertical federated learning framework for medical data"],"prefix":"10.1186","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0048-9876","authenticated-orcid":false,"given":"Fei","family":"Tang","sequence":"first","affiliation":[]},{"given":"Shikai","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Guowei","family":"Ling","sequence":"additional","affiliation":[]},{"given":"Jinyong","family":"Shan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,4]]},"reference":[{"key":"166_CR1","doi-asserted-by":"crossref","unstructured":"Abuadbba S, Kim K, Kim M, Thapa C, Camtepe SA, Gao Y, Kim H, Nepal S (2020) Can we use split learning on 1d CNN models for privacy preserving training? 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