2023
DOI: 10.1016/j.csi.2023.103734
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A secure and privacy-preserving word vector training scheme based on functional encryption with inner-product predicates

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Cited by 7 publications
(2 citation statements)
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“…To protect the data owned by multiple data providers, scheme [37] presents a privacy-preserving neural network prediction model, which can implement the prediction task safely in multiple-client model. Meanwhile, a machine learning method for training word vectors with security and privacy protection is proposed by Zhang et al [38]. It is worth mentioning that both schemes [37] and [38] use the innerproduct functional encryption algorithm to train the datasets provided by multiple participants, while ensuring the security of the datasets.…”
Section: B Application Of Cls In Vanetsmentioning
confidence: 99%
See 1 more Smart Citation
“…To protect the data owned by multiple data providers, scheme [37] presents a privacy-preserving neural network prediction model, which can implement the prediction task safely in multiple-client model. Meanwhile, a machine learning method for training word vectors with security and privacy protection is proposed by Zhang et al [38]. It is worth mentioning that both schemes [37] and [38] use the innerproduct functional encryption algorithm to train the datasets provided by multiple participants, while ensuring the security of the datasets.…”
Section: B Application Of Cls In Vanetsmentioning
confidence: 99%
“…Meanwhile, a machine learning method for training word vectors with security and privacy protection is proposed by Zhang et al [38]. It is worth mentioning that both schemes [37] and [38] use the innerproduct functional encryption algorithm to train the datasets provided by multiple participants, while ensuring the security of the datasets. In addition, Kang et al [39] propose a traceable and forward-secure attribute-based signature scheme with constant-size.…”
Section: B Application Of Cls In Vanetsmentioning
confidence: 99%