{"id":"https://openalex.org/W2015569667","doi":"https://doi.org/10.1145/2339530.2339564","title":"Differentially private transit data publication","display_name":"Differentially private transit data publication","publication_year":2012,"publication_date":"2012-08-12","ids":{"openalex":"https://openalex.org/W2015569667","doi":"https://doi.org/10.1145/2339530.2339564","mag":"2015569667"},"language":"en","primary_location":{"id":"doi:10.1145/2339530.2339564","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2339530.2339564","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5100403639","display_name":"Rui Chen","orcid":"https://orcid.org/0000-0002-3853-6308"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Rui Chen","raw_affiliation_strings":["Concordia University, Montreal, PQ, Canada","Concordia University, Montreal, PQ, Canada#TAB#"],"affiliations":[{"raw_affiliation_string":"Concordia University, Montreal, PQ, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Concordia University, Montreal, PQ, Canada#TAB#","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021788449","display_name":"Benjamin C. M. Fung","orcid":"https://orcid.org/0000-0001-8423-2906"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Benjamin C.M. Fung","raw_affiliation_strings":["Concordia University, Montreal, PQ, Canada","Concordia University, Montreal, PQ, Canada#TAB#"],"affiliations":[{"raw_affiliation_string":"Concordia University, Montreal, PQ, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Concordia University, Montreal, PQ, Canada#TAB#","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064963422","display_name":"Bipin C. Desai","orcid":"https://orcid.org/0000-0002-9142-7928"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Bipin C. Desai","raw_affiliation_strings":["Concordia University, Montreal, PQ, Canada","Concordia University, Montreal, PQ, Canada#TAB#"],"affiliations":[{"raw_affiliation_string":"Concordia University, Montreal, PQ, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Concordia University, Montreal, PQ, Canada#TAB#","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063640729","display_name":"N\u00e9riah M. Sossou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210137531","display_name":"Soci\u00e9t\u00e9 de Transport de Montr\u00e9al","ror":"https://ror.org/03xvqgb25","country_code":"CA","type":"other","lineage":["https://openalex.org/I4210137531"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"N\u00e9riah M. Sossou","raw_affiliation_strings":["Societe de transport de Montreal, Montreal, PQ, Canada"],"affiliations":[{"raw_affiliation_string":"Societe de transport de Montreal, Montreal, PQ, Canada","institution_ids":["https://openalex.org/I4210137531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100403639"],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":19.8897,"has_fulltext":false,"cited_by_count":248,"citation_normalized_percentile":{"value":0.99422558,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"213","last_page":"221"},"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/T11045","display_name":"Privacy, Security, and Data Protection","score":0.9869999885559082,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9853000044822693,"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/computer-science","display_name":"Computer science","score":0.80715012550354},{"id":"https://openalex.org/keywords/data-publishing","display_name":"Data publishing","score":0.7092005014419556},{"id":"https://openalex.org/keywords/trie","display_name":"Trie","score":0.6927428245544434},{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.6342875957489014},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6103615760803223},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5877721309661865},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.48444393277168274},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4800732731819153},{"id":"https://openalex.org/keywords/data-structure","display_name":"Data structure","score":0.4767668843269348},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.46577900648117065},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.463125079870224},{"id":"https://openalex.org/keywords/data-consistency","display_name":"Data consistency","score":0.4549938142299652},{"id":"https://openalex.org/keywords/smart-card","display_name":"Smart card","score":0.4425409734249115},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.44043636322021484},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.42329761385917664},{"id":"https://openalex.org/keywords/publishing","display_name":"Publishing","score":0.3021129369735718},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.2325393259525299},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.17936626076698303},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.12067750096321106}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.80715012550354},{"id":"https://openalex.org/C2781396290","wikidata":"https://www.wikidata.org/wiki/Q17051824","display_name":"Data publishing","level":3,"score":0.7092005014419556},{"id":"https://openalex.org/C190290938","wikidata":"https://www.wikidata.org/wiki/Q387015","display_name":"Trie","level":3,"score":0.6927428245544434},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.6342875957489014},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6103615760803223},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5877721309661865},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.48444393277168274},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4800732731819153},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.4767668843269348},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.46577900648117065},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.463125079870224},{"id":"https://openalex.org/C93361087","wikidata":"https://www.wikidata.org/wiki/Q4426698","display_name":"Data consistency","level":2,"score":0.4549938142299652},{"id":"https://openalex.org/C110406131","wikidata":"https://www.wikidata.org/wiki/Q41349","display_name":"Smart card","level":2,"score":0.4425409734249115},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.44043636322021484},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.42329761385917664},{"id":"https://openalex.org/C151719136","wikidata":"https://www.wikidata.org/wiki/Q3972943","display_name":"Publishing","level":2,"score":0.3021129369735718},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2325393259525299},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.17936626076698303},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.12067750096321106},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","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":1,"locations":[{"id":"doi:10.1145/2339530.2339564","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2339530.2339564","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W40555093","https://openalex.org/W1584003909","https://openalex.org/W1873763122","https://openalex.org/W1993116423","https://openalex.org/W2005107218","https://openalex.org/W2010362518","https://openalex.org/W2025483242","https://openalex.org/W2044985623","https://openalex.org/W2054922243","https://openalex.org/W2057576485","https://openalex.org/W2065846791","https://openalex.org/W2069857456","https://openalex.org/W2077217970","https://openalex.org/W2090431844","https://openalex.org/W2096558825","https://openalex.org/W2096870293","https://openalex.org/W2100921307","https://openalex.org/W2106303673","https://openalex.org/W2107041969","https://openalex.org/W2112340163","https://openalex.org/W2124612670","https://openalex.org/W2129157759","https://openalex.org/W2132768130","https://openalex.org/W2142922715","https://openalex.org/W2149545836","https://openalex.org/W2154086287","https://openalex.org/W2158454296","https://openalex.org/W2159024459","https://openalex.org/W2169570643","https://openalex.org/W2173102402","https://openalex.org/W2293703278","https://openalex.org/W3007260234","https://openalex.org/W3020511868","https://openalex.org/W4235076694"],"related_works":["https://openalex.org/W2961912854","https://openalex.org/W3094463742","https://openalex.org/W2915559073","https://openalex.org/W2884294946","https://openalex.org/W3209392453","https://openalex.org/W2951798800","https://openalex.org/W4241535341","https://openalex.org/W4311074414","https://openalex.org/W4234819362","https://openalex.org/W1484288940"],"abstract_inverted_index":{"With":[0],"the":[1,56,62,97,138,170],"wide":[2],"deployment":[3],"of":[4,21,26,37,96,101,150,162],"smart":[5],"card":[6],"automated":[7],"fare":[8],"collection":[9],"(SCAFC)":[10],"systems,":[11],"public":[12],"transit":[13,22,38,52,77],"agencies":[14],"have":[15],"been":[16],"benefiting":[17],"from":[18],"huge":[19],"volume":[20,149],"data,":[23,28,122],"a":[24,83,90,102,143],"kind":[25],"sequential":[27,121,151,177],"collected":[29],"every":[30],"day.":[31],"Yet,":[32],"improper":[33],"publishing":[34,147],"and":[35,175,193],"use":[36,95],"data":[39,53,78,152,158,165],"could":[40],"jeopardize":[41],"passengers'":[42],"privacy.":[43,155],"In":[44],"this":[45,136],"paper,":[46],"we":[47,93],"present":[48],"our":[49,133,188],"solution":[50,115,145],"to":[51,105,111,119,129,141,196],"publication":[54],"under":[55,153],"rigorous":[57],"differential":[58,154],"privacy":[59],"model":[60],"for":[61,146],"Soci\u00e9t\u00e9":[63],"de":[64,66],"transport":[65],"Montr\u00e9al":[67],"(STM).":[68],"We":[69,156],"propose":[70],"an":[71],"efficient":[72],"data-dependent":[73],"yet":[74],"differentially":[75],"private":[76],"sanitization":[79],"approach":[80,189],"based":[81],"on":[82,182],"hybrid-granularity":[84],"prefix":[85,103],"tree":[86,104],"structure.":[87],"Moreover,":[88],"as":[89],"post-processing":[91],"step,":[92],"make":[94],"inherent":[98],"consistency":[99],"constraints":[100],"conduct":[106],"constrained":[107],"inferences,":[108],"which":[109],"lead":[110],"better":[112],"utility.":[113],"Our":[114],"not":[116],"only":[117],"applies":[118],"general":[120],"but":[123],"also":[124],"can":[125],"be":[126],"seamlessly":[127],"extended":[128],"trajectory":[130],"data.":[131],"To":[132],"best":[134],"knowledge,":[135],"is":[137,194],"first":[139],"paper":[140],"introduce":[142],"practical":[144],"large":[148,197],"examine":[157],"utility":[159,192],"in":[160],"terms":[161],"two":[163],"popular":[164],"analysis":[166],"tasks":[167],"conducted":[168],"at":[169],"STM,":[171],"namely":[172],"count":[173],"queries":[174],"frequent":[176],"pattern":[178],"mining.":[179],"Extensive":[180],"experiments":[181],"real-life":[183],"STM":[184],"datasets":[185],"confirm":[186],"that":[187],"maintains":[190],"high":[191],"scalable":[195],"datasets.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":24},{"year":2019,"cited_by_count":28},{"year":2018,"cited_by_count":33},{"year":2017,"cited_by_count":25},{"year":2016,"cited_by_count":20},{"year":2015,"cited_by_count":15},{"year":2014,"cited_by_count":9},{"year":2013,"cited_by_count":17},{"year":2012,"cited_by_count":4}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}