Su Caiyu, Wei Jinri, Lei Yuan, Li Jiahui
Guangxi Vocational & Technical Institute of Industry, Nanning, Guangxi, China.
Universiti Pendidikan Sultan Idris, Tanjong Malim, Malaysia.
PeerJ Comput Sci. 2023 Aug 7;9:e1496. doi: 10.7717/peerj-cs.1496. eCollection 2023.
The rise of targeted advertising has led to frequent privacy data leaks, as advertisers are reluctant to share information to safeguard their interests. This has resulted in isolated data islands and model heterogeneity challenges. To address these issues, we have proposed a C-means clustering algorithm based on maximum average difference to improve the evaluation of the difference in distribution between local and global parameters. Additionally, we have introduced an innovative dynamic selection algorithm that leverages knowledge distillation and weight correction to reduce the impact of model heterogeneity. Our framework was tested on various datasets and its performance was evaluated using accuracy, loss, and AUC (area under the ROC curve) metrics. Results showed that the framework outperformed other models in terms of higher accuracy, lower loss, and better AUC while requiring the same computation time. Our research aims to provide a more reliable, controllable, and secure data sharing framework to enhance the efficiency and accuracy of targeted advertising.
定向广告的兴起导致隐私数据频繁泄露，因为广告商不愿共享信息以保护自身利益。这引发了孤立的数据孤岛和模型异质性挑战。为解决这些问题，我们提出了一种基于最大平均差异的C均值聚类算法，以改进对局部和全局参数分布差异的评估。此外，我们引入了一种创新的动态选择算法，该算法利用知识蒸馏和权重校正来减少模型异质性的影响。我们的框架在各种数据集上进行了测试，并使用准确率、损失和AUC（ROC曲线下面积）指标对其性能进行了评估。结果表明，该框架在准确率更高、损失更低、AUC更好的情况下，所需计算时间相同，优于其他模型。我们的研究旨在提供一个更可靠、可控且安全的数据共享框架，以提高定向广告的效率和准确性。
