文献类型：期刊文献

英文题名：UHRP: Uncertainty-Based Pruning Method for Anonymized Data Linear Regression

作者：Liu, Kun[1]; Liu, Wenyan[1]; Cheng, Junhong[1]; Lu, Xingjian[2]

机构：[1] School of Computer Science and Software Engineering, East China Normal University, Shanghai, China; [2] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2019

卷号：11448 LNCS

起止页码：19

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20192006911897)

语种：英文

外文关键词：Regression analysis - Data mining - Geometry

摘要：Anonymization method, as a kind of privacy protection technology for data publishing, has been heavily researched during the past twenty years. However, fewer researches have been conducted on making better use of the anonymized data for data mining. In this paper, we focus on training regression model using anonymized data and predicting on original samples using the trained model. Anonymized training instances are generally considered as hyper-rectangles, which is different from most machine learning tasks. We propose several hyper-rectangle vectorization methods that are compatible with both anonymized data and original data for model training. Anonymization brings additional uncertainty. To address this issue, we propose an Uncertainty-based Hyper-Rectangle Pruning method (UHRP) to reduce the disturbance introduced by anonymized data. In this method, we prune hyper-rectangle by its global uncertainty which is calculated from all uncertain attributes. Experiments show that a linear regressor trained on anonymized data could be expected to do as well as the model trained with original data under specific conditions. Experimental results also prove that our pruning method could further improve the model’s performance. ? 2019, Springer Nature Switzerland AG.