{"id":"https://openalex.org/W4417070096","doi":"https://doi.org/10.1145/3769764","title":"Benchmarking Differentially Private Tabular Data Synthesis: [Experiments & Analysis]","display_name":"Benchmarking Differentially Private Tabular Data Synthesis: [Experiments & Analysis]","publication_year":2025,"publication_date":"2025-12-04","ids":{"openalex":"https://openalex.org/W4417070096","doi":"https://doi.org/10.1145/3769764"},"language":"en","primary_location":{"id":"doi:10.1145/3769764","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3769764","pdf_url":null,"source":{"id":"https://openalex.org/S4387289859","display_name":"Proceedings of the ACM on Management of Data","issn_l":"2836-6573","issn":["2836-6573"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Management of Data","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1145/3769764","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021970877","display_name":"Kai Chen","orcid":"https://orcid.org/0009-0006-9461-8929"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kai Chen","raw_affiliation_strings":["University of Virginia, Charlottesville, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100328757","display_name":"Xiaochen Li","orcid":"https://orcid.org/0000-0002-3722-4783"},"institutions":[{"id":"https://openalex.org/I169335092","display_name":"University of North Carolina at Greensboro","ror":"https://ror.org/04fnxsj42","country_code":"US","type":"education","lineage":["https://openalex.org/I169335092"]},{"id":"https://openalex.org/I4693391","display_name":"Greensboro College","ror":"https://ror.org/02eb31840","country_code":"US","type":"education","lineage":["https://openalex.org/I4693391"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaochen Li","raw_affiliation_strings":["UNC Greensboro, Greensboro, USA"],"affiliations":[{"raw_affiliation_string":"UNC Greensboro, Greensboro, USA","institution_ids":["https://openalex.org/I4693391","https://openalex.org/I169335092"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045305534","display_name":"Chen Gong","orcid":"https://orcid.org/0000-0001-6178-4118"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen Gong","raw_affiliation_strings":["University of Virginia, Charlottesville, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051516514","display_name":"Ryan McKenna","orcid":"https://orcid.org/0000-0002-4950-1952"},"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/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ryan Mckenna","raw_affiliation_strings":["Google Research, Seattle, USA"],"affiliations":[{"raw_affiliation_string":"Google Research, Seattle, USA","institution_ids":["https://openalex.org/I1291425158","https://openalex.org/I58610484"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100610986","display_name":"Tianhao Wang","orcid":"https://orcid.org/0000-0002-9017-7947"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianhao Wang","raw_affiliation_strings":["University of Virginia, Charlottesville, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, USA","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5021970877"],"corresponding_institution_ids":["https://openalex.org/I51556381"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20169112,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":"6","first_page":"1","last_page":"25"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9534000158309937,"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":0.9534000158309937,"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":0.027499999850988388,"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/T11719","display_name":"Data Quality and Management","score":0.00430000014603138,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.8460999727249146},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5888000130653381},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4821000099182129},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4771000146865845},{"id":"https://openalex.org/keywords/safeguarding","display_name":"Safeguarding","score":0.3727000057697296},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.3508000075817108},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3310999870300293}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.8460999727249146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7516000270843506},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5888000130653381},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5044000148773193},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4821000099182129},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4771000146865845},{"id":"https://openalex.org/C2776743756","wikidata":"https://www.wikidata.org/wiki/Q5097921","display_name":"Safeguarding","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3508000075817108},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3310999870300293},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3125999867916107},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30660000443458557},{"id":"https://openalex.org/C2776542497","wikidata":"https://www.wikidata.org/wiki/Q5266672","display_name":"Development (topology)","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3009999990463257},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.2651999890804291}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3769764","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3769764","pdf_url":null,"source":{"id":"https://openalex.org/S4387289859","display_name":"Proceedings of the ACM on Management of Data","issn_l":"2836-6573","issn":["2836-6573"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Management of Data","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2504.14061","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.14061","pdf_url":"https://arxiv.org/pdf/2504.14061","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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"}],"best_oa_location":{"id":"doi:10.1145/3769764","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3769764","pdf_url":null,"source":{"id":"https://openalex.org/S4387289859","display_name":"Proceedings of the ACM on Management of Data","issn_l":"2836-6573","issn":["2836-6573"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Management of Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3792304751","display_name":null,"funder_award_id":"CNS-2220433, OAC-2319988","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G496887892","display_name":null,"funder_award_id":"cci award","funder_id":"https://openalex.org/F328363578","funder_display_name":"Commonwealth Cyber Initiative"}],"funders":[{"id":"https://openalex.org/F328363578","display_name":"Commonwealth Cyber Initiative","ror":null},{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2169401877","https://openalex.org/W2236011587","https://openalex.org/W2884174229","https://openalex.org/W2963378230","https://openalex.org/W2967880504","https://openalex.org/W3183767371","https://openalex.org/W3197295672","https://openalex.org/W4205654330","https://openalex.org/W4206471589","https://openalex.org/W4234726042","https://openalex.org/W4285149890","https://openalex.org/W4321168595","https://openalex.org/W4416549289","https://openalex.org/W6888931462"],"related_works":[],"abstract_inverted_index":{"Differentially":[0],"private":[1,15],"(DP)":[2],"tabular":[3,77],"data":[4,8,16,38,78,89],"synthesis":[5,79,94,120],"generates":[6],"artificial":[7],"that":[9,87,104],"preserves":[10],"the":[11,41,150,162],"statistical":[12,115],"properties":[13],"of":[14,23,43,140,154],"while":[17],"safeguarding":[18],"individual":[19],"privacy.":[20],"The":[21],"emergence":[22],"diverse":[24],"algorithms":[25],"in":[26,32,119],"recent":[27],"years":[28],"has":[29],"introduced":[30],"challenges":[31,70],"practical":[33],"applications,":[34],"such":[35],"as":[36,127,129],"inconsistent":[37],"processing":[39],"methods,":[40],"lack":[42],"in-depth":[44,138],"algorithm":[45],"analysis,":[46],"and":[47,93,98,152],"incomplete":[48],"comparisons":[49],"due":[50],"to":[51,60],"overlapping":[52],"development":[53],"timelines.":[54],"These":[55],"factors":[56],"create":[57],"significant":[58,106],"obstacles":[59],"selecting":[61],"appropriate":[62],"algorithms.":[63],"In":[64],"this":[65],"paper,":[66],"we":[67,135],"address":[68],"these":[69],"by":[71],"proposing":[72],"a":[73,83,105],"benchmark":[74],"for":[75],"evaluating":[76],"methods.":[80,113,133],"We":[81],"present":[82],"unified":[84],"evaluation":[85,102],"framework":[86],"integrates":[88],"preprocessing,":[90],"feature":[91],"selection,":[92],"modules,":[95],"facilitating":[96],"fair":[97],"comprehensive":[99],"comparisons.":[100],"Our":[101,157],"reveals":[103],"utility-efficiency":[107],"trade-off":[108],"exists":[109],"among":[110],"current":[111],"state-of-the-art":[112],"Some":[114],"methods":[116],"are":[117],"superior":[118],"utility,":[121],"but":[122],"their":[123],"efficiency":[124],"is":[125,159],"not":[126],"good":[128],"most":[130],"deep":[131],"learning-based":[132],"Furthermore,":[134],"conduct":[136],"an":[137],"analysis":[139],"each":[141],"module":[142],"with":[143],"experimental":[144],"validation,":[145],"offering":[146],"theoretical":[147],"insights":[148],"into":[149],"strengths":[151],"limitations":[153],"different":[155],"strategies.":[156],"code":[158],"open-sourced":[160],"via":[161],"link..":[163],"https://github.com/KaiChen9909/tab_bench":[164]},"counts_by_year":[],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2025-11-23T00:00:00"}