Yonghao Tang1, Zhiping Cai1,*, Qiang Liu1, Tongqing Zhou1, Qiang Ni2
CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2645-2656, 2023, DOI:10.32604/cmc.2023.032307
Abstract As an emerging discipline, machine learning has been widely used in artificial intelligence, education, meteorology and other fields. In the training of machine learning models, trainers need to use a large amount of practical data, which inevitably involves user privacy. Besides, by polluting the training data, a malicious adversary can poison the model, thus compromising model security. The data provider hopes that the model trainer can prove to them the confidentiality of the model. Trainer will be required to withdraw data when the trust collapses. In the meantime, trainers hope to forget the injected data More >
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