Martín Abadi, Ú. Erlingsson, I. Goodfellow, H. B. McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang
{"title":"关于机器学习系统中私有信息的保护:两种最新方法","authors":"Martín Abadi, Ú. Erlingsson, I. Goodfellow, H. B. McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang","doi":"10.1109/CSF.2017.10","DOIUrl":null,"url":null,"abstract":"The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy. However, older ideas about privacy may well remain valid and useful. This note reviews two recent works on privacy in the light of the wisdom of some of the early literature, in particular the principles distilled by Saltzer and Schroeder in the 1970s.","PeriodicalId":269696,"journal":{"name":"2017 IEEE 30th Computer Security Foundations Symposium (CSF)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"47","resultStr":"{\"title\":\"On the Protection of Private Information in Machine Learning Systems: Two Recent Approches\",\"authors\":\"Martín Abadi, Ú. Erlingsson, I. Goodfellow, H. B. McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang\",\"doi\":\"10.1109/CSF.2017.10\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy. However, older ideas about privacy may well remain valid and useful. This note reviews two recent works on privacy in the light of the wisdom of some of the early literature, in particular the principles distilled by Saltzer and Schroeder in the 1970s.\",\"PeriodicalId\":269696,\"journal\":{\"name\":\"2017 IEEE 30th Computer Security Foundations Symposium (CSF)\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"47\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 30th Computer Security Foundations Symposium (CSF)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSF.2017.10\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 30th Computer Security Foundations Symposium (CSF)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSF.2017.10","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On the Protection of Private Information in Machine Learning Systems: Two Recent Approches
The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy. However, older ideas about privacy may well remain valid and useful. This note reviews two recent works on privacy in the light of the wisdom of some of the early literature, in particular the principles distilled by Saltzer and Schroeder in the 1970s.
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