{"title":"Discrete Gaussian sampling for low-power devices","authors":"Shruti More, R. Katti","doi":"10.1109/PACRIM.2015.7334831","DOIUrl":null,"url":null,"abstract":"Sampling from the discrete Gaussian probability distribution is used in lattice-based cryptosystems. A need for faster and memory-efficient samplers has become a necessity for improving the performance of such cryptosystems. We propose a new algorithm for sampling from the Gaussian distribution that can efficiently change on-the-fly its speed/memory requirement. The Ziggurat algorithm that attempted to do this requires up to 1000 seconds of computation time to change memory requirements on-the-fly. Our algorithm eliminates this large computational overhead.","PeriodicalId":350052,"journal":{"name":"2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PACRIM.2015.7334831","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Sampling from the discrete Gaussian probability distribution is used in lattice-based cryptosystems. A need for faster and memory-efficient samplers has become a necessity for improving the performance of such cryptosystems. We propose a new algorithm for sampling from the Gaussian distribution that can efficiently change on-the-fly its speed/memory requirement. The Ziggurat algorithm that attempted to do this requires up to 1000 seconds of computation time to change memory requirements on-the-fly. Our algorithm eliminates this large computational overhead.