{"id":{"repo_id":"washington","oai_identifier":"oai:digital.lib.washington.edu:1773/35093"},"canonical_url":"https://search.dev.ndltd.org/etd/washington/oai:digital.lib.washington.edu:1773/35093","repository":{"repo_id":"washington","name":"University of Washington","base_url":"https://digital.lib.washington.edu/server/oai/request"},"display":{"title":"Discrete Gaussian Sampling for Low-Power Devices","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.","abstract_html":"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.","abstract_has_math":false,"creators":["More, Shruti Santosh"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Katti, Raj"],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-03-11","date_published":"2016-03-11","updated_at":"2026-07-24T05:58:18Z","subjects":["Discrete Gaussian Sampling; Lattice-based cryptography; Ziggurat algorithm"],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1773/35093","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Katti, Raj"]},{"key":"dc:creator","label":"Author","values":["More, Shruti Santosh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-03-11T22:35:53Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-03-11T22:35:53Z"]},{"key":"dc:date.issued","label":"Date","values":["2016-03-11"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Discrete Gaussian Sampling; Lattice-based cryptography; Ziggurat algorithm"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["More_washington_0250O_15221.pdf"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1773/35093"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (Master's)--University of Washington, 2015"]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Discrete Gaussian Sampling for Low-Power Devices"]}]}],"canonical_facts":{"dc:contributor.advisor":["Katti, Raj"],"dc:creator":["More, Shruti Santosh"],"dc:date.accessioned":["2016-03-11T22:35:53Z"],"dc:date.available":["2016-03-11T22:35:53Z"],"dc:date.issued":["2016-03-11"],"dc:description":["Thesis (Master's)--University of Washington, 2015"],"dc:description.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."],"dc:format.mimetype":["application/pdf"],"dc:identifier.other":["More_washington_0250O_15221.pdf"],"dc:identifier.uri":["http://hdl.handle.net/1773/35093"],"dc:language.iso":["en_US"],"dc:subject":["Discrete Gaussian Sampling; Lattice-based cryptography; Ziggurat algorithm"],"dc:title":["Discrete Gaussian Sampling for Low-Power Devices"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T05:58:18Z"}