{"id":"https://openalex.org/W1784469946","doi":"https://doi.org/10.1515/popets-2016-0015","title":"Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries","display_name":"Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries","publication_year":2016,"publication_date":"2016-05-06","ids":{"openalex":"https://openalex.org/W1784469946","doi":"https://doi.org/10.1515/popets-2016-0015","mag":"1784469946"},"language":"en","primary_location":{"id":"doi:10.1515/popets-2016-0015","is_oa":true,"landing_page_url":"https://doi.org/10.1515/popets-2016-0015","pdf_url":"https://petsymposium.org/popets/2016/popets-2016-0015.pdf","source":{"id":"https://openalex.org/S4210183172","display_name":"Proceedings on Privacy Enhancing Technologies","issn_l":"2299-0984","issn":["2299-0984"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings on Privacy Enhancing Technologies","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://petsymposium.org/popets/2016/popets-2016-0015.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076026636","display_name":"Giulia Fanti","orcid":"https://orcid.org/0000-0002-7671-2624"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]},{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Giulia Fanti","raw_affiliation_strings":["U.C. Berkeley","University of Illinois Urbana-Champaign, Urbana, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"U.C. Berkeley","institution_ids":["https://openalex.org/I95457486"]},{"raw_affiliation_string":"University of Illinois Urbana-Champaign, Urbana, United States","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050326458","display_name":"Vasyl Pihur","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]},{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vasyl Pihur","raw_affiliation_strings":["Google","Johns Hopkins University, Baltimore, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Johns Hopkins University, Baltimore, United States","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024723282","display_name":"\u00dalfar Erlingsson","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"\u00dalfar Erlingsson","raw_affiliation_strings":["Google","Google (United States), Mountain View, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google (United States), Mountain View, United States","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.5715,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":{"value":0.96775528,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"2016","issue":"3","first_page":"41","last_page":"61"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13030","display_name":"Survey Sampling and Estimation Techniques","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.7976593375205994},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7830416560173035},{"id":"https://openalex.org/keywords/randomized-response","display_name":"Randomized response","score":0.7065974473953247},{"id":"https://openalex.org/keywords/crowdsourcing","display_name":"Crowdsourcing","score":0.6221493482589722},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5913951396942139},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5361893177032471},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.4500647783279419},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41846713423728943},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.41770797967910767},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3375725746154785},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3372623920440674},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3345896601676941},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16750958561897278},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.09217086434364319},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0812070369720459}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.7976593375205994},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7830416560173035},{"id":"https://openalex.org/C2776441110","wikidata":"https://www.wikidata.org/wiki/Q1436628","display_name":"Randomized response","level":3,"score":0.7065974473953247},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.6221493482589722},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5913951396942139},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5361893177032471},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.4500647783279419},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41846713423728943},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.41770797967910767},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3375725746154785},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3372623920440674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3345896601676941},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16750958561897278},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.09217086434364319},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0812070369720459},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1515/popets-2016-0015","is_oa":true,"landing_page_url":"https://doi.org/10.1515/popets-2016-0015","pdf_url":"https://petsymposium.org/popets/2016/popets-2016-0015.pdf","source":{"id":"https://openalex.org/S4210183172","display_name":"Proceedings on Privacy Enhancing Technologies","issn_l":"2299-0984","issn":["2299-0984"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings on Privacy Enhancing Technologies","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1503.01214","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1503.01214","pdf_url":"https://arxiv.org/pdf/1503.01214","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:1784469946","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1503.01214.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1503.01214","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1503.01214","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1515/popets-2016-0015","is_oa":true,"landing_page_url":"https://doi.org/10.1515/popets-2016-0015","pdf_url":"https://petsymposium.org/popets/2016/popets-2016-0015.pdf","source":{"id":"https://openalex.org/S4210183172","display_name":"Proceedings on Privacy Enhancing Technologies","issn_l":"2299-0984","issn":["2299-0984"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings on Privacy Enhancing Technologies","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5699999928474426,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1784469946.pdf","grobid_xml":"https://content.openalex.org/works/W1784469946.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W5855678","https://openalex.org/W45828277","https://openalex.org/W58212258","https://openalex.org/W1658965807","https://openalex.org/W1917787295","https://openalex.org/W1981029888","https://openalex.org/W1986293063","https://openalex.org/W2004915866","https://openalex.org/W2013823004","https://openalex.org/W2022097286","https://openalex.org/W2049633694","https://openalex.org/W2053801139","https://openalex.org/W2104803737","https://openalex.org/W2106882672","https://openalex.org/W2113431598","https://openalex.org/W2146673169","https://openalex.org/W2147435839","https://openalex.org/W2911978475","https://openalex.org/W2949758198","https://openalex.org/W2951827590","https://openalex.org/W3103108607","https://openalex.org/W7048738093"],"related_works":["https://openalex.org/W2964225135","https://openalex.org/W3102407811","https://openalex.org/W1873763122","https://openalex.org/W2498120800","https://openalex.org/W2760279430","https://openalex.org/W2950414350","https://openalex.org/W2186689790","https://openalex.org/W2564480471","https://openalex.org/W2190633754","https://openalex.org/W2817941585","https://openalex.org/W3192904428","https://openalex.org/W2962880290","https://openalex.org/W3159988801","https://openalex.org/W2997147031","https://openalex.org/W2120582309","https://openalex.org/W2085472312","https://openalex.org/W3202122949","https://openalex.org/W2795198231","https://openalex.org/W2469282401","https://openalex.org/W2293703278"],"abstract_inverted_index":{"Abstract":[0],"Techniques":[1],"based":[2],"on":[3,51],"randomized":[4],"response":[5],"enable":[6,99],"the":[7,34,78,83,110],"collection":[8],"of":[9,33,37,40,55,85,113,137],"potentially":[10],"sensitive":[11],"data":[12],"from":[13],"clients":[14],"in":[15,58],"a":[16,38,52,73],"privacy-preserving":[17,43],"manner":[18],"with":[19,117],"strong":[20],"local":[21,130],"differential":[22,131],"privacy":[23,132],"guarantees.":[24],"A":[25],"recent":[26],"such":[27],"technology,":[28],"RAPPOR":[29,79],"[12],":[30],"enables":[31,82],"estimation":[32,48,84],"marginal":[35],"frequencies":[36],"set":[39],"strings":[41,89],"via":[42],"crowdsourcing.":[44],"However,":[45],"this":[46,60,69],"original":[47],"process":[49],"relies":[50],"known":[53],"dictionary":[54,61,103],"possible":[56],"strings;":[57],"practice,":[59],"can":[62,125],"be":[63,96,126],"extremely":[64],"large":[65],"and/or":[66],"unknown.":[67],"In":[68],"paper,":[70],"we":[71,90,94,105],"propose":[72],"novel":[74],"decoding":[75],"algorithm":[76],"for":[77,108,134],"mechanism":[80],"that":[81],"\u201cunknown":[86],"unknowns,\u201d":[87],"i.e.,":[88],"do":[91],"not":[92,122],"know":[93],"should":[95],"estimating.":[97],"To":[98],"learning":[100,135],"without":[101],"explicit":[102],"knowledge,":[104],"develop":[106],"methodology":[107],"estimating":[109],"joint":[111],"distribution":[112],"multiple":[114],"variables":[115],"collected":[116],"RAPPOR.":[118],"Our":[119],"contributions":[120],"are":[121],"RAPPOR-specific,":[123],"and":[124],"generalized":[127],"to":[128],"other":[129],"mechanisms":[133],"distributions":[136],"string-valued":[138],"random":[139],"variables.":[140]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":4}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}