{"title":"基于非线性核的支持向量机在水平分区数据上的隐私保护","authors":"Hwanjo Yu, Xiaoqian Jiang, Jaideep Vaidya","doi":"10.1145/1141277.1141415","DOIUrl":null,"url":null,"abstract":"Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects. What we need is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid results, while providing guarantees on the non-disclosure of data. Support vector machine classification is one of the most widely used classification methodologies in data mining and machine learning. It is based on solid theoretical foundations and has wide practical application. This paper proposes a privacy-preserving solution for support vector machine (SVM) classification, PP-SVM for short. Our solution constructs the global SVM classification model from the data distributed at multiple parties, without disclosing the data of each party to others. We assume that data is horizontally partitioned -- each party collects the same features of information for different data objects. We quantify the security and efficiency of the proposed method, and highlight future challenges.","PeriodicalId":269830,"journal":{"name":"Proceedings of the 2006 ACM symposium on Applied computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2006-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"220","resultStr":"{\"title\":\"Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data\",\"authors\":\"Hwanjo Yu, Xiaoqian Jiang, Jaideep Vaidya\",\"doi\":\"10.1145/1141277.1141415\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects. What we need is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid results, while providing guarantees on the non-disclosure of data. Support vector machine classification is one of the most widely used classification methodologies in data mining and machine learning. It is based on solid theoretical foundations and has wide practical application. This paper proposes a privacy-preserving solution for support vector machine (SVM) classification, PP-SVM for short. Our solution constructs the global SVM classification model from the data distributed at multiple parties, without disclosing the data of each party to others. We assume that data is horizontally partitioned -- each party collects the same features of information for different data objects. We quantify the security and efficiency of the proposed method, and highlight future challenges.\",\"PeriodicalId\":269830,\"journal\":{\"name\":\"Proceedings of the 2006 ACM symposium on Applied computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"220\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2006 ACM symposium on Applied computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1141277.1141415\",\"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 of the 2006 ACM symposium on Applied computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1141277.1141415","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data
Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects. What we need is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid results, while providing guarantees on the non-disclosure of data. Support vector machine classification is one of the most widely used classification methodologies in data mining and machine learning. It is based on solid theoretical foundations and has wide practical application. This paper proposes a privacy-preserving solution for support vector machine (SVM) classification, PP-SVM for short. Our solution constructs the global SVM classification model from the data distributed at multiple parties, without disclosing the data of each party to others. We assume that data is horizontally partitioned -- each party collects the same features of information for different data objects. We quantify the security and efficiency of the proposed method, and highlight future challenges.