{"id":"https://openalex.org/W4391093127","doi":"https://doi.org/10.1109/bigdata59044.2023.10386841","title":"IDMU: Impact Driven Machine Unlearning","display_name":"IDMU: Impact Driven Machine Unlearning","publication_year":2023,"publication_date":"2023-12-15","ids":{"openalex":"https://openalex.org/W4391093127","doi":"https://doi.org/10.1109/bigdata59044.2023.10386841"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata59044.2023.10386841","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata59044.2023.10386841","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","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/A5065749078","display_name":"Shubhi Asthana","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shubhi Asthana","raw_affiliation_strings":["IBM Research,San Jose,US","IBM Research, San Jose, US"],"affiliations":[{"raw_affiliation_string":"IBM Research,San Jose,US","institution_ids":["https://openalex.org/I1341412227"]},{"raw_affiliation_string":"IBM Research, San Jose, US","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100389714","display_name":"Bing Zhang","orcid":"https://orcid.org/0000-0003-1911-8466"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bing Zhang","raw_affiliation_strings":["IBM Research,San Jose,US","IBM Research, San Jose, US"],"affiliations":[{"raw_affiliation_string":"IBM Research,San Jose,US","institution_ids":["https://openalex.org/I1341412227"]},{"raw_affiliation_string":"IBM Research, San Jose, US","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059002634","display_name":"Ruchi Mahindru","orcid":"https://orcid.org/0000-0002-4711-8829"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ruchi Mahindru","raw_affiliation_strings":["IBM Research,San Jose,US","IBM Research, San Jose, US"],"affiliations":[{"raw_affiliation_string":"IBM Research,San Jose,US","institution_ids":["https://openalex.org/I1341412227"]},{"raw_affiliation_string":"IBM Research, San Jose, US","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070925303","display_name":"Indervir Singh Banipal","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Indervir Singh Banipal","raw_affiliation_strings":["IBM Watson,San Jose,US","IBM Watson, San Jose, US"],"affiliations":[{"raw_affiliation_string":"IBM Watson,San Jose,US","institution_ids":["https://openalex.org/I1341412227"]},{"raw_affiliation_string":"IBM Watson, San Jose, US","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032183063","display_name":"Pawan Chowdhary","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pawan Chowdhary","raw_affiliation_strings":["IBM Research,San Jose,US","IBM Research, San Jose, US"],"affiliations":[{"raw_affiliation_string":"IBM Research,San Jose,US","institution_ids":["https://openalex.org/I1341412227"]},{"raw_affiliation_string":"IBM Research, San Jose, US","institution_ids":["https://openalex.org/I1341412227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5065749078"],"corresponding_institution_ids":["https://openalex.org/I1341412227"],"apc_list":null,"apc_paid":null,"fwci":0.1748,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59675713,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"5146","last_page":"5155"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9983000159263611,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9983000159263611,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9975000023841858,"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":0.9961000084877014,"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/retraining","display_name":"Retraining","score":0.9043868780136108},{"id":"https://openalex.org/keywords/factoring","display_name":"Factoring","score":0.8355231285095215},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7390256524085999},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5742322206497192},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5312243103981018},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4608336091041565},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4451415538787842},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.42100101709365845},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4143090844154358},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4131510853767395},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.4125428795814514},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3530689477920532},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.2278466522693634},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13472184538841248},{"id":"https://openalex.org/keywords/accounting","display_name":"Accounting","score":0.0992937684059143}],"concepts":[{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.9043868780136108},{"id":"https://openalex.org/C177225278","wikidata":"https://www.wikidata.org/wiki/Q192674","display_name":"Factoring","level":2,"score":0.8355231285095215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7390256524085999},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5742322206497192},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5312243103981018},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4608336091041565},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4451415538787842},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.42100101709365845},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4143090844154358},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4131510853767395},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.4125428795814514},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3530689477920532},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2278466522693634},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13472184538841248},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0992937684059143},{"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/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C155202549","wikidata":"https://www.wikidata.org/wiki/Q178803","display_name":"International trade","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata59044.2023.10386841","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata59044.2023.10386841","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","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":40,"referenced_works":["https://openalex.org/W1488996941","https://openalex.org/W2052684427","https://openalex.org/W2102636708","https://openalex.org/W2109135024","https://openalex.org/W2803782214","https://openalex.org/W3001405476","https://openalex.org/W3035556513","https://openalex.org/W3035644192","https://openalex.org/W3128806248","https://openalex.org/W3130178918","https://openalex.org/W3154155772","https://openalex.org/W3175430527","https://openalex.org/W3175487048","https://openalex.org/W3176739818","https://openalex.org/W3202838631","https://openalex.org/W4205228770","https://openalex.org/W4205891028","https://openalex.org/W4220795335","https://openalex.org/W4221152663","https://openalex.org/W4244201901","https://openalex.org/W4283323859","https://openalex.org/W4284700465","https://openalex.org/W4293253127","https://openalex.org/W4294789994","https://openalex.org/W4299301436","https://openalex.org/W4306815767","https://openalex.org/W4306962637","https://openalex.org/W4318148030","https://openalex.org/W4318148192","https://openalex.org/W4385572399","https://openalex.org/W4392669907","https://openalex.org/W6769833289","https://openalex.org/W6787335730","https://openalex.org/W6790811189","https://openalex.org/W6797062389","https://openalex.org/W6810178482","https://openalex.org/W6842397358","https://openalex.org/W6842423523","https://openalex.org/W6845579281","https://openalex.org/W6847949883"],"related_works":["https://openalex.org/W2786391746","https://openalex.org/W2991483587","https://openalex.org/W4381430104","https://openalex.org/W2995102745","https://openalex.org/W4226059458","https://openalex.org/W2914559142","https://openalex.org/W1990237101","https://openalex.org/W4285322112","https://openalex.org/W3196471634","https://openalex.org/W4292794239"],"abstract_inverted_index":{"Enterprise":[0],"organizations":[1],"have":[2],"large":[3],"amounts":[4],"of":[5,43,85,90,103,109,134,137,156,166,183,188,198,208,215],"data":[6,28,45,65,138,149,190,209],"which":[7],"is":[8,101],"utilized":[9],"by":[10,202],"multiple":[11],"Machine":[12,76,129],"Learning":[13],"(ML)":[14],"models":[15,21],"over":[16,185,212],"various":[17],"software":[18],"frameworks.":[19],"These":[20],"provide":[22,122],"trends":[23],"and":[24,57,111,164,206],"insights":[25],"from":[26,48,160],"the":[27,49,54,69,83,92,116,135,161],"that":[29,99,131],"can":[30],"help":[31],"enterprises":[32],"define":[33],"business":[34,55],"rules":[35,56],"around":[36],"their":[37],"processes.":[38],"However,":[39],"if":[40],"certain":[41,107],"aspects":[42,108],"this":[44],"are":[46,174],"removed":[47],"datasets,":[50],"it":[51],"could":[52],"influence":[53],"policies":[58],"in":[59,82,105,153,204],"place.":[60],"When":[61],"a":[62,123,170,180,186,213],"user":[63],"requests":[64,140,211],"to":[66,121],"be":[67,73],"removed,":[68],"model":[70,199],"retraining":[71,91,200],"may":[72],"required":[74],"called":[75],"Unlearning":[77],"(MU).":[78],"Recent":[79],"research":[80],"works":[81],"area":[84],"MU":[86],"include":[87],"different":[88],"methods":[89],"machine":[93],"learning":[94],"models.":[95,117],"It":[96,193],"turns":[97],"out":[98],"there":[100],"lack":[102],"work":[104],"removing":[106],"data,":[110],"quantifying":[112],"its":[113],"impact":[114,136,207],"on":[115,169],"This":[118],"paper":[119],"aspires":[120],"novel":[124],"methodology":[125],"IDMU":[126,178],"(Impact":[127],"Driven":[128],"Unlearning)":[130],"performs":[132],"quantification":[133],"removal":[139,150,191,210],"while":[141],"performing":[142],"MU.":[143],"Our":[144],"method":[145,168],"provides":[146],"recommendations":[147],"for":[148],"requests,":[151],"factoring":[152,203],"underlying":[154],"features":[155],"data.":[157],"The":[158,176],"results":[159],"industrial":[162],"application":[163],"evaluation":[165],"our":[167],"financial":[171],"services":[172],"dataset":[173],"encouraging.":[175],"overall":[177],"had":[179],"mean":[181],"MAPE":[182],"10.25%":[184],"set":[187],"120":[189],"requests.":[192],"also":[194],"saved":[195],"~1900":[196],"hours":[197],"time":[201],"urgency":[205],"period":[214],"three":[216],"years.":[217]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
