{"id":"https://openalex.org/W2963122862","doi":"https://doi.org/10.1109/tsusc.2019.2930526","title":"MIASec: Enabling Data Indistinguishability Against Membership Inference Attacks in MLaaS","display_name":"MIASec: Enabling Data Indistinguishability Against Membership Inference Attacks in MLaaS","publication_year":2019,"publication_date":"2019-07-23","ids":{"openalex":"https://openalex.org/W2963122862","doi":"https://doi.org/10.1109/tsusc.2019.2930526","mag":"2963122862"},"language":"en","primary_location":{"id":"doi:10.1109/tsusc.2019.2930526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsusc.2019.2930526","pdf_url":null,"source":{"id":"https://openalex.org/S4210221417","display_name":"IEEE Transactions on Sustainable Computing","issn_l":"2377-3782","issn":["2377-3782","2377-3790"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Sustainable Computing","raw_type":"journal-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/A5100337659","display_name":"Chen Wang","orcid":"https://orcid.org/0000-0003-1963-4954"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chen Wang","raw_affiliation_strings":["School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-1963-4954","affiliations":[{"raw_affiliation_string":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104666790","display_name":"Gaoyang Liu","orcid":"https://orcid.org/0000-0003-2566-9360"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gaoyang Liu","raw_affiliation_strings":["School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053579397","display_name":"Haojun Huang","orcid":"https://orcid.org/0000-0002-4154-8529"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haojun Huang","raw_affiliation_strings":["School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-4154-8529","affiliations":[{"raw_affiliation_string":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012668540","display_name":"Weijie Feng","orcid":"https://orcid.org/0009-0005-1911-4790"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weijie Feng","raw_affiliation_strings":["School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101861025","display_name":"Kai Peng","orcid":"https://orcid.org/0000-0001-9910-4237"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Peng","raw_affiliation_strings":["School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-9910-4237","affiliations":[{"raw_affiliation_string":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009116003","display_name":"Lizhe Wang","orcid":"https://orcid.org/0000-0003-2766-0845"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lizhe Wang","raw_affiliation_strings":["School of Computer and Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-2766-0845","affiliations":[{"raw_affiliation_string":"School of Computer and Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100337659"],"corresponding_institution_ids":["https://openalex.org/I47720641"],"apc_list":null,"apc_paid":null,"fwci":3.0344,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.93297267,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"5","issue":"3","first_page":"365","last_page":"376"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998000264167786,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9958999752998352,"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/machine-learning","display_name":"Machine learning","score":0.7915173768997192},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.786628007888794},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7075777649879456},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6799860596656799},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5720726847648621},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4754868745803833},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.43370741605758667},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.43354320526123047},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3108821511268616}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7915173768997192},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.786628007888794},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7075777649879456},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6799860596656799},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5720726847648621},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4754868745803833},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.43370741605758667},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.43354320526123047},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3108821511268616}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsusc.2019.2930526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsusc.2019.2930526","pdf_url":null,"source":{"id":"https://openalex.org/S4210221417","display_name":"IEEE Transactions on Sustainable Computing","issn_l":"2377-3782","issn":["2377-3782","2377-3790"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Sustainable Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1532142744","display_name":null,"funder_award_id":"61872415","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2077566469","display_name":null,"funder_award_id":"61872416","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G365754280","display_name":null,"funder_award_id":"51479159","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4288848311","display_name":null,"funder_award_id":"61671216","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6201024728","display_name":null,"funder_award_id":"61702204","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6427088040","display_name":null,"funder_award_id":"61871436","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G781080216","display_name":null,"funder_award_id":"2019kfyXJJS017","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G8834534954","display_name":null,"funder_award_id":"51879210","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1473189865","https://openalex.org/W1853481526","https://openalex.org/W1873763122","https://openalex.org/W1993116423","https://openalex.org/W2011430131","https://openalex.org/W2031738616","https://openalex.org/W2040228409","https://openalex.org/W2051267297","https://openalex.org/W2123820077","https://openalex.org/W2285181575","https://openalex.org/W2461943168","https://openalex.org/W2532520288","https://openalex.org/W2535690855","https://openalex.org/W2603766943","https://openalex.org/W2605289356","https://openalex.org/W2617377769","https://openalex.org/W2620038827","https://openalex.org/W2621033546","https://openalex.org/W2681109946","https://openalex.org/W2701059868","https://openalex.org/W2753840555","https://openalex.org/W2757528734","https://openalex.org/W2767079719","https://openalex.org/W2767761285","https://openalex.org/W2778284298","https://openalex.org/W2786233556","https://openalex.org/W2790149281","https://openalex.org/W2793398195","https://openalex.org/W2799694080","https://openalex.org/W2884280357","https://openalex.org/W2930926105","https://openalex.org/W2948030332","https://openalex.org/W2963080984","https://openalex.org/W2963378725","https://openalex.org/W2963744840","https://openalex.org/W2963843518","https://openalex.org/W2964151798","https://openalex.org/W2964318098","https://openalex.org/W2969695741","https://openalex.org/W3001494301","https://openalex.org/W3103245149","https://openalex.org/W3137695714","https://openalex.org/W6628547770","https://openalex.org/W6638944511","https://openalex.org/W6639246211","https://openalex.org/W6696022951","https://openalex.org/W6744389324","https://openalex.org/W6746849571","https://openalex.org/W6747553010","https://openalex.org/W6749255846","https://openalex.org/W6750884629","https://openalex.org/W6751219861","https://openalex.org/W6753280856","https://openalex.org/W6763485134"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3014300295","https://openalex.org/W2799803467","https://openalex.org/W2950279864","https://openalex.org/W2753840555","https://openalex.org/W3214437258","https://openalex.org/W4287703079","https://openalex.org/W4206233339","https://openalex.org/W2995253937"],"abstract_inverted_index":{"The":[0,124],"emerging":[1],"of":[2,10,49,107,127,135,173,190,253],"machine":[3,42,58,72,163,214],"learning":[4,43,59,73,164,215],"has":[5,113],"massively":[6],"promoted":[7],"the":[8,38,46,56,68,77,90,104,108,114,132,139,144,147,152,161,171,177,184,187,198,205,239,249],"abilities":[9],"computational":[11],"sustainability":[12],"in":[13,61,89,122,138,176,201,261],"natural":[14],"resource":[15],"management":[16],"and":[17,26,52,66,111,150,193,222,228,251,258,263,266],"allocation.":[18],"Many":[19],"Internet":[20],"giants":[21],"such":[22,142],"as":[23,32],"Google,":[24],"Amazon,":[25],"Microsoft":[27],"now":[28],"provide":[29],"Machine":[30],"Learning":[31],"a":[33,86],"Service":[34],"(MLaaS)":[35],"to":[36,75,83,116,130],"meet":[37],"increasing":[39],"demand":[40],"for":[41],"services.":[44],"However,":[45],"prediction":[47,158],"results":[48,159,233],"training":[50,91,109,140,145,178,191,199],"data":[51,54,92,105,110,154,192,200,227],"testing":[53,148,194],"with":[55],"same":[57,162],"model":[60],"MLaaS":[62],"have":[63,155],"remarkable":[64],"differences,":[65],"thus":[67,182],"attackers":[69],"can":[70,102,181,237,247],"leverage":[71],"techniques":[74],"launch":[76],"so-called":[78],"membership":[79,119,240],"inference":[80,120,241],"attacks,":[81],"i.e.,":[82],"infer":[84],"whether":[85],"record":[87],"is":[88,129],"or":[93],"not.":[94],"In":[95,244],"this":[96],"paper,":[97],"we":[98],"propose":[99],"MIASec":[100,128,180,212,236,246],"that":[101,143,235],"guarantee":[103],"indistinguishability":[106],"thereby":[112,196],"ability":[115],"defend":[117,238],"against":[118],"attacks":[121,242,254],"MLaaS.":[123,224],"key":[125],"idea":[126],"narrow":[131],"dynamic":[133],"ranges":[134],"vital":[136,174],"features":[137,175],"data,":[141,146,149,179,195],"even":[151],"synthetic":[153],"almost":[156],"semblable":[157],"by":[160,218,256,264],"model.":[165],"With":[166],"elaborated":[167],"design":[168],"on":[169,213],"modifying":[170],"values":[172],"reduce":[183,248],"differences":[185],"between":[186],"model's":[188,206],"outcomes":[189],"protecting":[197],"effect":[202],"while":[203],"keeping":[204],"accuracy":[207],"stable.":[208],"We":[209],"empirically":[210],"evaluate":[211],"models":[216],"trained":[217],"off-line":[219],"neural":[220],"networks":[221],"on-line":[223],"Using":[225],"realistic":[226],"classification":[229],"tasks,":[230],"our":[231],"experiment":[232],"show":[234],"effectively.":[243],"particular,":[245],"precision":[250],"recall":[252],"respectively":[255],"11.7":[257],"15.4":[259],"percent":[260,268],"average,":[262],"18.6":[265],"21.8":[267],"at":[269],"best.":[270]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
