{"title":"集成多方计算和MapReduce基础设施的编程支持","authors":"Nikolaj Volgushev, A. Lapets, Azer Bestavros","doi":"10.1109/HOTWEB.2015.21","DOIUrl":null,"url":null,"abstract":"We describe and present a prototype of a distributed computational infrastructure and associated high-level programming language that allow multiple parties to leverage their own computational resources capable of supporting MapReduce [1] operations in combination with multi-party computation (MPC). Our architecture allows a programmer to author and compile a protocol using a uniform collection of standard constructs, even when that protocol involves computations that take place locally within each participant's MapReduce cluster as well as across all the participants using an MPC protocol. The high-level programming language provided to the user is accompanied by static analysis algorithms that allow the programmer to reason about the efficiency of the protocol before compiling and running it. We present two example applications demonstrating how such an infrastructure can be employed.","PeriodicalId":252318,"journal":{"name":"2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Programming Support for an Integrated Multi-Party Computation and MapReduce Infrastructure\",\"authors\":\"Nikolaj Volgushev, A. Lapets, Azer Bestavros\",\"doi\":\"10.1109/HOTWEB.2015.21\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe and present a prototype of a distributed computational infrastructure and associated high-level programming language that allow multiple parties to leverage their own computational resources capable of supporting MapReduce [1] operations in combination with multi-party computation (MPC). Our architecture allows a programmer to author and compile a protocol using a uniform collection of standard constructs, even when that protocol involves computations that take place locally within each participant's MapReduce cluster as well as across all the participants using an MPC protocol. The high-level programming language provided to the user is accompanied by static analysis algorithms that allow the programmer to reason about the efficiency of the protocol before compiling and running it. We present two example applications demonstrating how such an infrastructure can be employed.\",\"PeriodicalId\":252318,\"journal\":{\"name\":\"2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HOTWEB.2015.21\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HOTWEB.2015.21","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Programming Support for an Integrated Multi-Party Computation and MapReduce Infrastructure
We describe and present a prototype of a distributed computational infrastructure and associated high-level programming language that allow multiple parties to leverage their own computational resources capable of supporting MapReduce [1] operations in combination with multi-party computation (MPC). Our architecture allows a programmer to author and compile a protocol using a uniform collection of standard constructs, even when that protocol involves computations that take place locally within each participant's MapReduce cluster as well as across all the participants using an MPC protocol. The high-level programming language provided to the user is accompanied by static analysis algorithms that allow the programmer to reason about the efficiency of the protocol before compiling and running it. We present two example applications demonstrating how such an infrastructure can be employed.