Ontology type: schema:ScholarlyArticle
2019-05
AUTHORSChengli Zhang, Wenping Ma, Feifei Zhao
ABSTRACTThe “strong trapdoor function for lattice” has been constructed by Daniele Micciancio and Chris Peikert in EUROCRYPT 2012, which is simple, efficient, and easy to implement. In this paper, we present a new trapdoor function based on “ring learning with errors” problem (Ring-LWE) on lattice, and simultaneously the corresponding efficient inverse algorithm is given which involves two sub-algorithms: the trapdoor inverse algorithm and the iterative inverse algorithm. Our trapdoor function for lattice based on Ring-LWE is simultaneously more simple and efficient because of the ring structure. In addition to these advantages, our algorithm extends the parameters, and this can make our trapdoor function have a wider choice of applications. More... »
PAGES1821-1827
http://scigraph.springernature.com/pub.10.1007/s12652-018-0718-2
DOIhttp://dx.doi.org/10.1007/s12652-018-0718-2
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1101106102
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0802",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Computation Theory and Mathematics",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Xidian University",
"id": "https://www.grid.ac/institutes/grid.440736.2",
"name": [
"State Key Laboratory of Integrated Services Networks, Xidian University, 710071, Xi\u2019an, People\u2019s Republic of China"
],
"type": "Organization"
},
"familyName": "Zhang",
"givenName": "Chengli",
"id": "sg:person.011337061237.06",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011337061237.06"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Xidian University",
"id": "https://www.grid.ac/institutes/grid.440736.2",
"name": [
"State Key Laboratory of Integrated Services Networks, Xidian University, 710071, Xi\u2019an, People\u2019s Republic of China"
],
"type": "Organization"
},
"familyName": "Ma",
"givenName": "Wenping",
"id": "sg:person.011613037021.66",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011613037021.66"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Xidian University",
"id": "https://www.grid.ac/institutes/grid.440736.2",
"name": [
"State Key Laboratory of Integrated Services Networks, Xidian University, 710071, Xi\u2019an, People\u2019s Republic of China"
],
"type": "Organization"
},
"familyName": "Zhao",
"givenName": "Feifei",
"id": "sg:person.013766773444.37",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013766773444.37"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/978-3-642-10366-7_36",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004771430",
"https://doi.org/10.1007/978-3-642-10366-7_36"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-10366-7_36",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004771430",
"https://doi.org/10.1007/978-3-642-10366-7_36"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/11681878_8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005543732",
"https://doi.org/10.1007/11681878_8"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/11681878_8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005543732",
"https://doi.org/10.1007/11681878_8"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/1568318.1568324",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013302643"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/3-540-44647-8_13",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014375289",
"https://doi.org/10.1007/3-540-44647-8_13"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/3-540-44647-8_13",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014375289",
"https://doi.org/10.1007/3-540-44647-8_13"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-13190-5_2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023605606",
"https://doi.org/10.1007/978-3-642-13190-5_2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-13190-5_2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023605606",
"https://doi.org/10.1007/978-3-642-13190-5_2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-13190-5_1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025305533",
"https://doi.org/10.1007/978-3-642-13190-5_1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-13190-5_1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025305533",
"https://doi.org/10.1007/978-3-642-13190-5_1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-29011-4_41",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033795788",
"https://doi.org/10.1007/978-3-642-29011-4_41"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/11761679_27",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036281518",
"https://doi.org/10.1007/11761679_27"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/11761679_27",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036281518",
"https://doi.org/10.1007/11761679_27"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-540-24676-3_13",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038265457",
"https://doi.org/10.1007/978-3-540-24676-3_13"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/11426639_7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038491737",
"https://doi.org/10.1007/11426639_7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/11426639_7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038491737",
"https://doi.org/10.1007/11426639_7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-38553-7_22",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039150645",
"https://doi.org/10.1007/978-3-642-38553-7_22"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-13013-7_25",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039389499",
"https://doi.org/10.1007/978-3-642-13013-7_25"
],
"type": "CreativeWork"
},
{
"id": "https://app.dimensions.ai/details/publication/pub.1044722064",
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-1-4419-9383-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044722064",
"https://doi.org/10.1007/978-1-4419-9383-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-1-4419-9383-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044722064",
"https://doi.org/10.1007/978-1-4419-9383-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-22792-9_29",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049197805",
"https://doi.org/10.1007/978-3-642-22792-9_29"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-22792-9_29",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049197805",
"https://doi.org/10.1007/978-3-642-22792-9_29"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tit.1976.1055638",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061647862"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1137/080733954",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1062855106"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1137/120868669",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1062869195"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1137/s0097539705447360",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1062879617"
],
"type": "CreativeWork"
}
],
"datePublished": "2019-05",
"datePublishedReg": "2019-05-01",
"description": "The \u201cstrong trapdoor function for lattice\u201d has been constructed by Daniele Micciancio and Chris Peikert in EUROCRYPT 2012, which is simple, efficient, and easy to implement. In this paper, we present a new trapdoor function based on \u201cring learning with errors\u201d problem (Ring-LWE) on lattice, and simultaneously the corresponding efficient inverse algorithm is given which involves two sub-algorithms: the trapdoor inverse algorithm and the iterative inverse algorithm. Our trapdoor function for lattice based on Ring-LWE is simultaneously more simple and efficient because of the ring structure. In addition to these advantages, our algorithm extends the parameters, and this can make our trapdoor function have a wider choice of applications.",
"genre": "research_article",
"id": "sg:pub.10.1007/s12652-018-0718-2",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1043999",
"issn": [
"1868-5137",
"1868-5145"
],
"name": "Journal of Ambient Intelligence and Humanized Computing",
"type": "Periodical"
},
{
"issueNumber": "5",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "10"
}
],
"name": "Trapdoor function based on the Ring-LWE and applications in communications",
"pagination": "1821-1827",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"c2ec3f2f56e83c956228b8ae17d23b2ba33806e0f154e2b33239e2a193532a3a"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s12652-018-0718-2"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1101106102"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s12652-018-0718-2",
"https://app.dimensions.ai/details/publication/pub.1101106102"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T13:56",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000371_0000000371/records_130814_00000005.jsonl",
"type": "ScholarlyArticle",
"url": "https://link.springer.com/10.1007%2Fs12652-018-0718-2"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s12652-018-0718-2'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s12652-018-0718-2'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12652-018-0718-2'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12652-018-0718-2'
This table displays all metadata directly associated to this object as RDF triples.
144 TRIPLES
21 PREDICATES
46 URIs
19 LITERALS
7 BLANK NODES