Yonghao Tang¹, Zhiping Cai^1,*, 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 to regain security when finding… More >

Shoujian Yu¹, Zhenchi Jie¹, Guowen Wu¹, Hong Zhang¹, Shigen Shen^2,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1729-1751, 2023, DOI:10.32604/iasc.2023.039911

Abstract In recent years, the type and quantity of news are growing rapidly, and it is not easy for users to find the news they are interested in the massive amount of news. A news recommendation system can score and predict the candidate news, and finally recommend the news with high scores to users. However, existing user models usually only consider users’ long-term interests and ignore users’ recent interests, which affects users’ usage experience. Therefore, this paper introduces gated recurrent unit (GRU) sequence network to capture users’ short-term interests and combines users’ short-term interests and long-term interests to characterize users. While… More >

Jin Wang¹, Ying Liu¹, Shuying Rao¹, R. Simon Sherratt², Jinbin Hu^1,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3849-3865, 2023, DOI:10.32604/cmc.2023.037150

Abstract Data security and user privacy have become crucial elements in multi-tenant data centers. Various traffic types in the multi-tenant data center in the cloud environment have their characteristics and requirements. In the data center network (DCN), short and long flows are sensitive to low latency and high throughput, respectively. The traditional security processing approaches, however, neglect these characteristics and requirements. This paper proposes a fine-grained security enhancement mechanism (SEM) to solve the problem of heterogeneous traffic and reduce the traffic completion time (FCT) of short flows while ensuring the security of multi-tenant traffic transmission. Specifically, for short flows in DCN,… More >