{"title":"Streamforce:将流数据外包到云的访问控制实施","authors":"Tien Tuan Anh Dinh, Anwitaman Datta","doi":"10.1145/2557547.2557556","DOIUrl":null,"url":null,"abstract":"In this paper, we focus on the problem of data privacy on the cloud, particularly on access controls over stream data. The nature of stream data and the complexity of sharing data make access control a more challenging issue than in traditional archival databases. We present Streamforce -- a system allowing data owners to securely outsource their data to an untrusted (curious-but-honest) cloud. The owner specifies fine-grained policies which are enforced by the cloud. The latter performs most of the heavy computations, while learning nothing about the data content. To this end, we employ a number of encryption schemes, including deterministic encryption, proxy-based attribute based encryption and sliding-window encryption. In Streamforce, access control policies are modeled as secure continuous queries, which entails minimal changes to existing stream processing engines, and allows for easy expression of a wide-range of policies. In particular, Streamforce comes with a number of secure query operators including Map, Filter, Join and Aggregate. Finally, we implement Streamforce over an open-source stream processing engine (Esper) and evaluate its performance on a cloud platform. The results demonstrate practical performance for many real-world applications, and although the security overhead is visible, Streamforce is highly scalable.","PeriodicalId":90472,"journal":{"name":"CODASPY : proceedings of the ... ACM conference on data and application security and privacy. ACM Conference on Data and Application Security & Privacy","volume":"66 1","pages":"13-24"},"PeriodicalIF":0.0000,"publicationDate":"2013-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Streamforce: outsourcing access control enforcement for stream data to the clouds\",\"authors\":\"Tien Tuan Anh Dinh, Anwitaman Datta\",\"doi\":\"10.1145/2557547.2557556\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we focus on the problem of data privacy on the cloud, particularly on access controls over stream data. The nature of stream data and the complexity of sharing data make access control a more challenging issue than in traditional archival databases. We present Streamforce -- a system allowing data owners to securely outsource their data to an untrusted (curious-but-honest) cloud. The owner specifies fine-grained policies which are enforced by the cloud. The latter performs most of the heavy computations, while learning nothing about the data content. To this end, we employ a number of encryption schemes, including deterministic encryption, proxy-based attribute based encryption and sliding-window encryption. In Streamforce, access control policies are modeled as secure continuous queries, which entails minimal changes to existing stream processing engines, and allows for easy expression of a wide-range of policies. In particular, Streamforce comes with a number of secure query operators including Map, Filter, Join and Aggregate. Finally, we implement Streamforce over an open-source stream processing engine (Esper) and evaluate its performance on a cloud platform. The results demonstrate practical performance for many real-world applications, and although the security overhead is visible, Streamforce is highly scalable.\",\"PeriodicalId\":90472,\"journal\":{\"name\":\"CODASPY : proceedings of the ... ACM conference on data and application security and privacy. ACM Conference on Data and Application Security & Privacy\",\"volume\":\"66 1\",\"pages\":\"13-24\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CODASPY : proceedings of the ... ACM conference on data and application security and privacy. ACM Conference on Data and Application Security & Privacy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2557547.2557556\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CODASPY : proceedings of the ... ACM conference on data and application security and privacy. ACM Conference on Data and Application Security & Privacy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2557547.2557556","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Streamforce: outsourcing access control enforcement for stream data to the clouds
In this paper, we focus on the problem of data privacy on the cloud, particularly on access controls over stream data. The nature of stream data and the complexity of sharing data make access control a more challenging issue than in traditional archival databases. We present Streamforce -- a system allowing data owners to securely outsource their data to an untrusted (curious-but-honest) cloud. The owner specifies fine-grained policies which are enforced by the cloud. The latter performs most of the heavy computations, while learning nothing about the data content. To this end, we employ a number of encryption schemes, including deterministic encryption, proxy-based attribute based encryption and sliding-window encryption. In Streamforce, access control policies are modeled as secure continuous queries, which entails minimal changes to existing stream processing engines, and allows for easy expression of a wide-range of policies. In particular, Streamforce comes with a number of secure query operators including Map, Filter, Join and Aggregate. Finally, we implement Streamforce over an open-source stream processing engine (Esper) and evaluate its performance on a cloud platform. The results demonstrate practical performance for many real-world applications, and although the security overhead is visible, Streamforce is highly scalable.