{"title":"安全地发现多元线性关系","authors":"Ningning Wu, Jing Zhang, Li Ning","doi":"10.1109/IAW.2005.1495989","DOIUrl":null,"url":null,"abstract":"This paper considers the privacy-preserving cooperative linear system of equations (PPC-LSE) problem in a large, heterogeneous, distributed database scenario. It proposes a privacy-preserving algorithm to discover multivariate linear relationship based on factor analysis. Compared with other PPC-LSE algorithms, the proposed algorithm not only significantly reduces the communication cost, but also avoids the random matrix generation of either party to hide private information.","PeriodicalId":252208,"journal":{"name":"Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Discovering multivariate linear relationship securely\",\"authors\":\"Ningning Wu, Jing Zhang, Li Ning\",\"doi\":\"10.1109/IAW.2005.1495989\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper considers the privacy-preserving cooperative linear system of equations (PPC-LSE) problem in a large, heterogeneous, distributed database scenario. It proposes a privacy-preserving algorithm to discover multivariate linear relationship based on factor analysis. Compared with other PPC-LSE algorithms, the proposed algorithm not only significantly reduces the communication cost, but also avoids the random matrix generation of either party to hide private information.\",\"PeriodicalId\":252208,\"journal\":{\"name\":\"Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAW.2005.1495989\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAW.2005.1495989","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Discovering multivariate linear relationship securely
This paper considers the privacy-preserving cooperative linear system of equations (PPC-LSE) problem in a large, heterogeneous, distributed database scenario. It proposes a privacy-preserving algorithm to discover multivariate linear relationship based on factor analysis. Compared with other PPC-LSE algorithms, the proposed algorithm not only significantly reduces the communication cost, but also avoids the random matrix generation of either party to hide private information.
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