Ontology type: schema:ScholarlyArticle Open Access: True
2020-11-23
AUTHORSRan Canetti, Benjamin Fuller, Omer Paneth, Leonid Reyzin, Adam Smith
ABSTRACTFuzzy extractors (Dodis et al., in Advances in cryptology—EUROCRYPT 2014, Springer, Berlin, 2014, pp 93–110) convert repeated noisy readings of a secret into the same uniformly distributed key. To eliminate noise, they require an initial enrollment phase that takes the first noisy reading of the secret and produces a nonsecret helper string to be used in subsequent readings. Reusable fuzzy extractors (Boyen, in Proceedings of the 11th ACM conference on computer and communications security, CCS, ACM, New York, 2004, pp 82–91) remain secure even when this initial enrollment phase is repeated multiple times with noisy versions of the same secret, producing multiple helper strings (for example, when a single person’s biometric is enrolled with multiple unrelated organizations). We construct the first reusable fuzzy extractor that makes no assumptions about how multiple readings of the source are correlated. The extractor works for binary strings with Hamming noise; it achieves computational security under the existence of digital lockers (Canetti and Dakdouk, in Advances in cryptology—EUROCRYPT 2008, Springer, Berlin, 2008, pp 489–508). It is simple and tolerates near-linear error rates. Our reusable extractor is secure for source distributions of linear min-entropy rate. The construction is also secure for sources with much lower entropy rates—lower than those supported by prior (nonreusable) constructions—assuming that the distribution has some additional structure, namely, that random subsequences of the source have sufficient minentropy. Structure beyond entropy is necessary to support distributions with low entropy rates. We then explore further how different structural properties of a noisy source can be used to construct fuzzy extractors when the error rates are high, building a computationally secure and an information-theoretically secure construction for large-alphabet sources. More... »
PAGES2
http://scigraph.springernature.com/pub.10.1007/s00145-020-09367-8
DOIhttp://dx.doi.org/10.1007/s00145-020-09367-8
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1132941875
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/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0804",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Data Format",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Tel Aviv University, Tel Aviv, Israel",
"id": "http://www.grid.ac/institutes/grid.12136.37",
"name": [
"Boston University, Boston, USA",
"Tel Aviv University, Tel Aviv, Israel"
],
"type": "Organization"
},
"familyName": "Canetti",
"givenName": "Ran",
"id": "sg:person.012320111457.74",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012320111457.74"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Connecticut, Storrs, USA",
"id": "http://www.grid.ac/institutes/grid.63054.34",
"name": [
"University of Connecticut, Storrs, USA"
],
"type": "Organization"
},
"familyName": "Fuller",
"givenName": "Benjamin",
"id": "sg:person.013244656177.72",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013244656177.72"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Massachusetts Institute of Technology, Cambridge, USA",
"id": "http://www.grid.ac/institutes/grid.116068.8",
"name": [
"Massachusetts Institute of Technology, Cambridge, USA"
],
"type": "Organization"
},
"familyName": "Paneth",
"givenName": "Omer",
"id": "sg:person.014073524511.68",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014073524511.68"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Boston University, Boston, USA",
"id": "http://www.grid.ac/institutes/grid.189504.1",
"name": [
"Boston University, Boston, USA"
],
"type": "Organization"
},
"familyName": "Reyzin",
"givenName": "Leonid",
"id": "sg:person.016627532062.10",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016627532062.10"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Boston University, Boston, USA",
"id": "http://www.grid.ac/institutes/grid.189504.1",
"name": [
"Boston University, Boston, USA"
],
"type": "Organization"
},
"familyName": "Smith",
"givenName": "Adam",
"id": "sg:person.013307226666.21",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013307226666.21"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/978-3-642-55220-5_6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1034736534",
"https://doi.org/10.1007/978-3-642-55220-5_6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/3-540-69053-0_15",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047413612",
"https://doi.org/10.1007/3-540-69053-0_15"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bfb0052244",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012602811",
"https://doi.org/10.1007/bfb0052244"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-1-4471-0515-2_27",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043279260",
"https://doi.org/10.1007/978-1-4471-0515-2_27"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s102070100006",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008722744",
"https://doi.org/10.1007/s102070100006"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-14623-7_34",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012218885",
"https://doi.org/10.1007/978-3-642-14623-7_34"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-540-45146-4_4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001240361",
"https://doi.org/10.1007/978-3-540-45146-4_4"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-662-53887-6_10",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1084920151",
"https://doi.org/10.1007/978-3-662-53887-6_10"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/11426639_9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047382454",
"https://doi.org/10.1007/11426639_9"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-662-44381-1_7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1006934572",
"https://doi.org/10.1007/978-3-662-44381-1_7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/11894063_29",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005869106",
"https://doi.org/10.1007/11894063_29"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-319-60080-2_1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1089967528",
"https://doi.org/10.1007/978-3-319-60080-2_1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-04138-9_24",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037474974",
"https://doi.org/10.1007/978-3-642-04138-9_24"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-319-93638-3_2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1104575151",
"https://doi.org/10.1007/978-3-319-93638-3_2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/11535218_29",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010535182",
"https://doi.org/10.1007/11535218_29"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-540-24676-3_2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1035756543",
"https://doi.org/10.1007/978-3-540-24676-3_2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-42033-7_10",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014500804",
"https://doi.org/10.1007/978-3-642-42033-7_10"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-14623-7_28",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052251590",
"https://doi.org/10.1007/978-3-642-14623-7_28"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-22670-0_24",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036241586",
"https://doi.org/10.1007/978-3-642-22670-0_24"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-540-78967-3_28",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1020737814",
"https://doi.org/10.1007/978-3-540-78967-3_28"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-662-49890-3_5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014328379",
"https://doi.org/10.1007/978-3-662-49890-3_5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-540-78967-3_27",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037449861",
"https://doi.org/10.1007/978-3-540-78967-3_27"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bfb0052255",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033233326",
"https://doi.org/10.1007/bfb0052255"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bfb0034847",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1006305048",
"https://doi.org/10.1007/bfb0034847"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-030-30215-3_23",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1120756873",
"https://doi.org/10.1007/978-3-030-30215-3_23"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00453-016-0218-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1006324643",
"https://doi.org/10.1007/s00453-016-0218-8"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10623-018-0459-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1100618927",
"https://doi.org/10.1007/s10623-018-0459-4"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-319-63697-9_23",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1091024670",
"https://doi.org/10.1007/978-3-319-63697-9_23"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-11145-7_14",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012296037",
"https://doi.org/10.1007/978-3-642-11145-7_14"
],
"type": "CreativeWork"
}
],
"datePublished": "2020-11-23",
"datePublishedReg": "2020-11-23",
"description": "Fuzzy extractors (Dodis et al., in Advances in cryptology\u2014EUROCRYPT 2014, Springer, Berlin, 2014, pp 93\u2013110) convert repeated noisy readings of a secret into the same uniformly distributed key. To eliminate noise, they require an initial enrollment phase that takes the first noisy reading of the secret and produces a nonsecret helper string to be used in subsequent readings. Reusable fuzzy extractors (Boyen, in Proceedings of the 11th ACM conference on computer and communications security, CCS, ACM, New York, 2004, pp 82\u201391) remain secure even when this initial enrollment phase is repeated multiple times with noisy versions of the same secret, producing multiple helper strings (for example, when a single person\u2019s biometric is enrolled with multiple unrelated organizations). We construct the first reusable fuzzy extractor that makes no assumptions about how multiple readings of the source are correlated. The extractor works for binary strings with Hamming noise; it achieves computational security under the existence of digital lockers (Canetti and Dakdouk, in Advances in cryptology\u2014EUROCRYPT 2008, Springer, Berlin, 2008, pp 489\u2013508). It is simple and tolerates near-linear error rates. Our reusable extractor is secure for source distributions of linear min-entropy rate. The construction is also secure for sources with much lower entropy rates\u2014lower than those supported by prior (nonreusable) constructions\u2014assuming that the distribution has some additional structure, namely, that random subsequences of the source have sufficient minentropy. Structure beyond entropy is necessary to support distributions with low entropy rates. We then explore further how different structural properties of a noisy source can be used to construct fuzzy extractors when the error rates are high, building a computationally secure and an information-theoretically secure construction for large-alphabet sources.",
"genre": "article",
"id": "sg:pub.10.1007/s00145-020-09367-8",
"inLanguage": "en",
"isAccessibleForFree": true,
"isFundedItemOf": [
{
"id": "sg:grant.3140831",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.3850679",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.3849556",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.3848304",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.7438402",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.7912009",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.3107274",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.3114602",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.3582257",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.3092831",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.3084991",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.3849724",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.3114586",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1136278",
"issn": [
"0933-2790",
"1432-1378"
],
"name": "Journal of Cryptology",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "34"
}
],
"keywords": [
"fuzzy extractor",
"initial enrollment phase",
"noisy readings",
"enrollment phase",
"large alphabet sources",
"reusable fuzzy extractor",
"error rate",
"computational security",
"digital locker",
"secure construction",
"same secret",
"noisy version",
"noisy sources",
"lower entropy rate",
"extractor",
"min-entropy rate",
"binary strings",
"random subsequences",
"secrets",
"minentropy",
"multiple times",
"security",
"string",
"entropy rate",
"subsequences",
"noise",
"key",
"information",
"entropy distribution",
"lockers",
"additional structure",
"construction",
"version",
"source distribution",
"different structural properties",
"tolerates",
"source",
"entropy",
"multiple readings",
"reading",
"time",
"assumption",
"structure",
"distribution",
"subsequent reading",
"phase",
"rate",
"structural properties",
"converts",
"existence",
"properties"
],
"name": "Reusable Fuzzy Extractors for Low-Entropy Distributions",
"pagination": "2",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1132941875"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s00145-020-09367-8"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00145-020-09367-8",
"https://app.dimensions.ai/details/publication/pub.1132941875"
],
"sdDataset": "articles",
"sdDatePublished": "2022-06-01T22:20",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/article/article_836.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s00145-020-09367-8"
}
]
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/s00145-020-09367-8'
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/s00145-020-09367-8'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00145-020-09367-8'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00145-020-09367-8'
This table displays all metadata directly associated to this object as RDF triples.
289 TRIPLES
22 PREDICATES
105 URIs
68 LITERALS
6 BLANK NODES