{"title":"一个匿名数据集的质量评估","authors":"Sam Fletcher, M. Islam","doi":"10.1109/ICPR.2014.618","DOIUrl":null,"url":null,"abstract":"In this study we argue that the traditional approach of evaluating the information quality of an anonymized (or otherwise modified) dataset is questionable. We propose a novel and simple approach to evaluate the information quality of a modified dataset, and thereby the quality of techniques that modify data. We carry out experiments on eleven datasets and the empirical results strongly support our arguments. We also present some supplementary measures to our approach that provide additional insight into the information quality of modified data.","PeriodicalId":142159,"journal":{"name":"2014 22nd International Conference on Pattern Recognition","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Quality Evaluation of an Anonymized Dataset\",\"authors\":\"Sam Fletcher, M. Islam\",\"doi\":\"10.1109/ICPR.2014.618\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study we argue that the traditional approach of evaluating the information quality of an anonymized (or otherwise modified) dataset is questionable. We propose a novel and simple approach to evaluate the information quality of a modified dataset, and thereby the quality of techniques that modify data. We carry out experiments on eleven datasets and the empirical results strongly support our arguments. We also present some supplementary measures to our approach that provide additional insight into the information quality of modified data.\",\"PeriodicalId\":142159,\"journal\":{\"name\":\"2014 22nd International Conference on Pattern Recognition\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 22nd International Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.2014.618\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 22nd International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2014.618","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this study we argue that the traditional approach of evaluating the information quality of an anonymized (or otherwise modified) dataset is questionable. We propose a novel and simple approach to evaluate the information quality of a modified dataset, and thereby the quality of techniques that modify data. We carry out experiments on eleven datasets and the empirical results strongly support our arguments. We also present some supplementary measures to our approach that provide additional insight into the information quality of modified data.