{"id":"https://openalex.org/W2438870418","doi":"https://doi.org/10.1109/icde.2016.7498241","title":"Differentially private multi-party high-dimensional data publishing","display_name":"Differentially private multi-party high-dimensional data publishing","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2438870418","doi":"https://doi.org/10.1109/icde.2016.7498241","mag":"2438870418"},"language":"en","primary_location":{"id":"doi:10.1109/icde.2016.7498241","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde.2016.7498241","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036865453","display_name":"Sen Su","orcid":"https://orcid.org/0000-0003-4266-7527"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sen Su","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102841330","display_name":"Peng Tang","orcid":"https://orcid.org/0000-0003-1304-8401"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Tang","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062327704","display_name":"Xiang Cheng","orcid":"https://orcid.org/0000-0001-6556-2264"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Cheng","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100403639","display_name":"Rui Chen","orcid":"https://orcid.org/0000-0002-3853-6308"},"institutions":[{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rui Chen","raw_affiliation_strings":["Samsung Research, America"],"affiliations":[{"raw_affiliation_string":"Samsung Research, America","institution_ids":["https://openalex.org/I4210101778"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024183684","display_name":"Zequn Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zequn Wu","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5036865453"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":8.5694,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.9766487,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"205","last_page":"216"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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":1.0,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9699000120162964,"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"}},{"id":"https://openalex.org/T10237","display_name":"Cryptography and Data Security","score":0.9686999917030334,"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/differential-privacy","display_name":"Differential privacy","score":0.9548836350440979},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8073217272758484},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.6760510206222534},{"id":"https://openalex.org/keywords/differential","display_name":"Differential (mechanical device)","score":0.5398293137550354},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4741339683532715},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4733933210372925},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.4418973922729492},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4299708604812622},{"id":"https://openalex.org/keywords/frontier","display_name":"Frontier","score":0.4212955832481384},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4131971001625061},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3581210970878601},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23456019163131714},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22265443205833435}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.9548836350440979},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8073217272758484},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6760510206222534},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.5398293137550354},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4741339683532715},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4733933210372925},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.4418973922729492},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4299708604812622},{"id":"https://openalex.org/C2778571376","wikidata":"https://www.wikidata.org/wiki/Q1355821","display_name":"Frontier","level":2,"score":0.4212955832481384},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4131971001625061},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3581210970878601},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23456019163131714},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22265443205833435},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icde.2016.7498241","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde.2016.7498241","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W5855678","https://openalex.org/W72671121","https://openalex.org/W1511694993","https://openalex.org/W1577921908","https://openalex.org/W1873763122","https://openalex.org/W1952161176","https://openalex.org/W1966287333","https://openalex.org/W1993924693","https://openalex.org/W1995564021","https://openalex.org/W2006453614","https://openalex.org/W2031253505","https://openalex.org/W2038873345","https://openalex.org/W2046776785","https://openalex.org/W2049599124","https://openalex.org/W2067400450","https://openalex.org/W2069169703","https://openalex.org/W2096480010","https://openalex.org/W2096803423","https://openalex.org/W2096870293","https://openalex.org/W2100257623","https://openalex.org/W2108368547","https://openalex.org/W2110287632","https://openalex.org/W2123733729","https://openalex.org/W2123820077","https://openalex.org/W2134479759","https://openalex.org/W2142406320","https://openalex.org/W2149545836","https://openalex.org/W2159024459","https://openalex.org/W2165157425","https://openalex.org/W2364035448","https://openalex.org/W2525846285","https://openalex.org/W2539384137","https://openalex.org/W3120740533","https://openalex.org/W6634357985","https://openalex.org/W6639246211","https://openalex.org/W6640782454","https://openalex.org/W6675236921","https://openalex.org/W6676246221"],"related_works":["https://openalex.org/W2347401120","https://openalex.org/W2261902776","https://openalex.org/W2310010941","https://openalex.org/W1988132375","https://openalex.org/W2334292868","https://openalex.org/W579144800","https://openalex.org/W2147233680","https://openalex.org/W2046798653","https://openalex.org/W2069525434","https://openalex.org/W2021057137"],"abstract_inverted_index":{"In":[0,20,66,190],"this":[1,52],"paper,":[2],"we":[3,54,168,205,241],"study":[4],"the":[5,23,29,68,71,75,82,104,110,114,136,139,152,159,165,187,195,223,232],"novel":[6],"problem":[7],"of":[8,25,61,102,133,138,161,197],"publishing":[9],"high-dimensional":[10],"data":[11,34,263],"in":[12,86,227],"a":[13,26,38,56,87,92,147,170,208,217,255],"distributed":[14],"multi-party":[15],"environment":[16],"under":[17],"differential":[18],"privacy.":[19],"particular,":[21,191],"with":[22,180,265],"assistance":[24],"semi-trusted":[27],"curator,":[28],"involved":[30],"parties":[31,69,111,120,153],"(i.e.,":[32],"local":[33,48,250],"owners)":[35],"collectively":[36],"generate":[37],"synthetic":[39,93],"integrated":[40,83],"dataset":[41,84,94,257],"while":[42],"satisfying":[43],"\u03b5-differential":[44,246],"privacy":[45,239,247],"for":[46,248],"any":[47,249],"dataset.":[49,251],"To":[50,157],"solve":[51],"problem,":[53],"present":[55],"differentially":[57],"private":[58],"sequential":[59,88,105],"update":[60,106,155],"Bayesian":[62,76],"network":[63,77],"(DP-SUBN)":[64],"solution.":[65],"DP-SUBN,":[67],"and":[70,163,214],"curator":[72],"collaboratively":[73],"identify":[74],"\u2115":[78,162],"that":[79,109,231,243,259],"best":[80],"fits":[81],"D":[85],"manner,":[89],"from":[90],"which":[91,142],"can":[95,112,143],"then":[96,215],"be":[97,144],"generated.":[98],"The":[99,131],"fundamental":[100],"advantage":[101],"adopting":[103],"manner":[107],"is":[108,135,235],"treat":[113],"statistical":[115],"results":[116],"provided":[117],"by":[118],"previous":[119],"as":[121,146],"their":[122],"prior":[123],"knowledge":[124,149],"to":[125,128,150,154,185,192,221,229],"direct":[126],"how":[127],"learn":[129],"\u2115.":[130,156],"core":[132],"DP-SUBN":[134,244,260],"construction":[137,174],"search":[140,172,188],"frontier,":[141],"seen":[145],"priori":[148],"guide":[151],"improve":[158],"fitness":[160],"reduce":[164],"communication":[166,267],"cost,":[167],"introduce":[169,216],"correlation-aware":[171],"frontier":[173],"(CSFC)":[175],"approach,":[176],"where":[177],"attribute":[178,198],"pairs":[179,199],"strong":[181],"correlations":[182,196],"are":[183],"used":[184,226],"construct":[186],"frontier.":[189],"privately":[193],"quantify":[194],"without":[200],"introducing":[201],"too":[202],"much":[203],"noise,":[204],"first":[206],"propose":[207],"non-overlapping":[209],"covering":[210],"design":[211],"(NOCD)":[212],"method,":[213],"dynamic":[218],"programming":[219],"method":[220],"find":[222],"optimal":[224],"parameters":[225],"NOCD":[228],"ensure":[230],"injected":[233],"noise":[234],"minimum.":[236],"Through":[237],"formal":[238],"analysis,":[240],"show":[242],"satisfies":[245],"Extensive":[252],"experiments":[253],"on":[254],"real":[256],"demonstrate":[258],"offers":[261],"desirable":[262],"utility":[264],"low":[266],"cost.":[268]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
