{"id":"https://openalex.org/W4297847624","doi":"https://doi.org/10.48550/arxiv.2209.07400","title":"Private Synthetic Data for Multitask Learning and Marginal Queries","display_name":"Private Synthetic Data for Multitask Learning and Marginal Queries","publication_year":2022,"publication_date":"2022-09-15","ids":{"openalex":"https://openalex.org/W4297847624","doi":"https://doi.org/10.48550/arxiv.2209.07400"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2209.07400","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.07400","pdf_url":"https://arxiv.org/pdf/2209.07400","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":"","raw_type":null},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2209.07400","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009962130","display_name":"Giuseppe Vietri","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vietri, Giuseppe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021107714","display_name":"C\u00e9dric Archambeau","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Archambeau, Cedric","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060422342","display_name":"Serg\u00fcl Ayd\u00f6re","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aydore, Sergul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040753367","display_name":"William Brown","orcid":"https://orcid.org/0000-0002-9367-6418"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Brown, William","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029730907","display_name":"Michael Kearns","orcid":"https://orcid.org/0000-0001-7569-0147"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kearns, Michael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057693522","display_name":"Aaron Roth","orcid":"https://orcid.org/0000-0002-0586-0515"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roth, Aaron","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015478643","display_name":"Ankit Siva","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Siva, Ankit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101678361","display_name":"Shuai Tang","orcid":"https://orcid.org/0000-0002-9584-2838"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Shuai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5001070941","display_name":"Zhiwei Steven Wu","orcid":"https://orcid.org/0000-0002-8125-8227"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Zhiwei Steven","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9993000030517578,"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":0.9993000030517578,"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/T10237","display_name":"Cryptography and Data Security","score":0.9940000176429749,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9523000121116638,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7053683996200562},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6814278960227966},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.6508762836456299},{"id":"https://openalex.org/keywords/cardinality","display_name":"Cardinality (data modeling)","score":0.6094024777412415},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5503336191177368},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.5388554930686951},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4642430543899536},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.43992263078689575},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42604780197143555},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3951278030872345},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3630153238773346}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7053683996200562},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6814278960227966},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.6508762836456299},{"id":"https://openalex.org/C87117476","wikidata":"https://www.wikidata.org/wiki/Q362383","display_name":"Cardinality (data modeling)","level":2,"score":0.6094024777412415},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5503336191177368},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.5388554930686951},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4642430543899536},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.43992263078689575},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42604780197143555},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3951278030872345},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3630153238773346},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2209.07400","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.07400","pdf_url":"https://arxiv.org/pdf/2209.07400","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":"","raw_type":null},{"id":"doi:10.48550/arxiv.2209.07400","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2209.07400","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":"pmh:oai:arXiv.org:2209.07400","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.07400","pdf_url":"https://arxiv.org/pdf/2209.07400","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":"","raw_type":null},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6700000166893005,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2931688134","https://openalex.org/W2377919138","https://openalex.org/W2378857091","https://openalex.org/W2999756192","https://openalex.org/W103652678","https://openalex.org/W4226090359","https://openalex.org/W2059697060","https://openalex.org/W4382701072","https://openalex.org/W4256502920","https://openalex.org/W4229333355"],"abstract_inverted_index":{"We":[0],"provide":[1],"a":[2,39,118,141,145],"differentially":[3],"private":[4],"algorithm":[5,27,149],"for":[6,12,67,78,159,194],"producing":[7],"synthetic":[8,84,130,157],"data":[9,85,158],"simultaneously":[10],"useful":[11],"multiple":[13],"tasks:":[14],"marginal":[15,161,188],"queries":[16,91,189],"and":[17,98,129,167,181,190],"multitask":[18,146],"machine":[19],"learning":[20],"(ML).":[21],"A":[22],"key":[23],"innovation":[24],"in":[25,36,125,144,186],"our":[26],"is":[28,65,121,133,137],"the":[29,76,105,109,123,127,134,177],"ability":[30],"to":[31,38,49,82,139,153],"directly":[32],"handle":[33],"numerical":[34,47,96,168],"features,":[35,97],"contrast":[37],"number":[40],"of":[41,89,111,113],"related":[42],"prior":[43],"approaches":[44],"which":[45],"require":[46],"features":[48,57],"be":[50],"first":[51],"converted":[52],"into":[53],"{high":[54],"cardinality}":[55],"categorical":[56,166],"via":[58],"{a":[59],"binning":[60,63,79],"strategy}.":[61],"Higher":[62],"granularity":[64],"required":[66],"better":[68],"accuracy,":[69],"but":[70],"this":[71],"negatively":[72],"impacts":[73],"scalability.":[74],"Eliminating":[75],"need":[77],"allows":[80,151],"us":[81,152],"produce":[83,154],"preserving":[86],"large":[87],"numbers":[88],"statistical":[90],"such":[92],"as":[93],"marginals":[94],"on":[95],"class":[99,115],"conditional":[100],"linear":[101,142,191],"threshold":[102],"queries.":[103],"Preserving":[104],"latter":[106],"means":[107],"that":[108,136,163],"fraction":[110],"points":[112],"each":[114],"label":[116],"above":[117],"particular":[119],"half-space":[120],"roughly":[122],"same":[124],"both":[126,165,187],"real":[128],"data.":[131],"This":[132],"property":[135],"needed":[138],"train":[140],"classifier":[143],"setting.":[147],"Our":[148,170],"also":[150],"high":[155],"quality":[156],"mixed":[160],"queries,":[162],"combine":[164],"features.":[169],"method":[171],"consistently":[172],"runs":[173],"2-5x":[174],"faster":[175],"than":[176],"best":[178],"comparable":[179],"techniques,":[180],"provides":[182],"significant":[183],"accuracy":[184],"improvements":[185],"prediction":[192],"tasks":[193],"mixed-type":[195],"datasets.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2023,"cited_by_count":2}],"updated_date":"2026-08-08T07:41:36.138363","created_date":"2025-10-10T00:00:00"}
