{"title":"Preserving Differential Privacy in Degree-Correlation based Graph Generation","authors":"WangYue, WUXin-Tao","doi":"10.5555/2612167.2612168","DOIUrl":null,"url":null,"abstract":"Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as cluster coefficient often have high sensitivity, which is d...","PeriodicalId":44319,"journal":{"name":"Transactions on Data Privacy","volume":"1 1","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions on Data Privacy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5555/2612167.2612168","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 15
Abstract
Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as cluster coefficient often have high sensitivity, which is d...