{"title":"Privacy preserving health data processing","authors":"Anders Andersen, K. Y. Yigzaw, Randi Karlsen","doi":"10.1109/HealthCom.2014.7001845","DOIUrl":null,"url":null,"abstract":"The usage of electronic health data from different sources for statistical analysis requires a toolset where the legal, security and privacy concerns have been taken into consideration. The health data are typically located at different general practices and hospitals. The data analysis consists of local processing at these locations, and the locations become nodes in a computing graph. To support the legal, security and privacy concerns, the proposed toolset for statistical analysis of health data uses a combination of secure multi-party computation (SMC) algorithms, symmetric and public key encryption, and public key infrastructure (PKI) with certificates and a certificate authority (CA). The proposed toolset should cover a wide range of data analysis with different data distributions. To achieve this, large set of possible SMC algorithms and computing graphs have to be supported.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HealthCom.2014.7001845","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
Abstract
The usage of electronic health data from different sources for statistical analysis requires a toolset where the legal, security and privacy concerns have been taken into consideration. The health data are typically located at different general practices and hospitals. The data analysis consists of local processing at these locations, and the locations become nodes in a computing graph. To support the legal, security and privacy concerns, the proposed toolset for statistical analysis of health data uses a combination of secure multi-party computation (SMC) algorithms, symmetric and public key encryption, and public key infrastructure (PKI) with certificates and a certificate authority (CA). The proposed toolset should cover a wide range of data analysis with different data distributions. To achieve this, large set of possible SMC algorithms and computing graphs have to be supported.