A. Mkhinini, P. Maistri, R. Leveugle, R. Tourki, Mohsen Machhout
{"title":"一种灵活的基于神经网络的全同态加密大多项式乘法器","authors":"A. Mkhinini, P. Maistri, R. Leveugle, R. Tourki, Mohsen Machhout","doi":"10.1109/IDT.2016.7843028","DOIUrl":null,"url":null,"abstract":"In the era of the cloud computing, homomorphic encryption allows remote data processing while preserving confidentiality. Its main drawback, however, is the huge complexity in terms of operand size and computation time, which makes hardware acceleration desirable in order to achieve acceptable performance. In this paper, we present a flexible modular polynomial multiplier implemented through a high-level synthesis flow. We show that flexibility does not come at a price, and the proposed solution is competitive against custom designs.","PeriodicalId":131600,"journal":{"name":"2016 11th International Design & Test Symposium (IDT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A flexible RNS-based large polynomial multiplier for Fully Homomorphic Encryption\",\"authors\":\"A. Mkhinini, P. Maistri, R. Leveugle, R. Tourki, Mohsen Machhout\",\"doi\":\"10.1109/IDT.2016.7843028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the era of the cloud computing, homomorphic encryption allows remote data processing while preserving confidentiality. Its main drawback, however, is the huge complexity in terms of operand size and computation time, which makes hardware acceleration desirable in order to achieve acceptable performance. In this paper, we present a flexible modular polynomial multiplier implemented through a high-level synthesis flow. We show that flexibility does not come at a price, and the proposed solution is competitive against custom designs.\",\"PeriodicalId\":131600,\"journal\":{\"name\":\"2016 11th International Design & Test Symposium (IDT)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 11th International Design & Test Symposium (IDT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IDT.2016.7843028\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 11th International Design & Test Symposium (IDT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IDT.2016.7843028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A flexible RNS-based large polynomial multiplier for Fully Homomorphic Encryption
In the era of the cloud computing, homomorphic encryption allows remote data processing while preserving confidentiality. Its main drawback, however, is the huge complexity in terms of operand size and computation time, which makes hardware acceleration desirable in order to achieve acceptable performance. In this paper, we present a flexible modular polynomial multiplier implemented through a high-level synthesis flow. We show that flexibility does not come at a price, and the proposed solution is competitive against custom designs.