{"id":"https://openalex.org/W7108339685","doi":"https://doi.org/10.5281/zenodo.17784600","title":"Coded Machine Unlearning using Machine Learning","display_name":"Coded Machine Unlearning using Machine Learning","publication_year":2022,"publication_date":"2022-04-01","ids":{"openalex":"https://openalex.org/W7108339685","doi":"https://doi.org/10.5281/zenodo.17784600"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.17784600","doi":"https://doi.org/10.5281/zenodo.17784600","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.17784600","pdf_url":null,"source":{"id":"https://openalex.org/S4404674083","display_name":"International Journal of Leading Research Publication.","issn_l":"2582-8010","issn":["2582-8010"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.5281/zenodo.17784600","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120662096","display_name":"Prasanth Tirumalasetty","orcid":"https://orcid.org/0009-0001-1294-2375","observed_orcids":["https://orcid.org/0009-0001-1294-2375"]},"institutions":[{"id":"https://openalex.org/I4210130704","display_name":"University of Michigan\u2013Dearborn","ror":"https://ror.org/035wtm547","country_code":"US","type":"education","lineage":["https://openalex.org/I4210130704"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tirumalasetty, Prasanth","raw_affiliation_strings":["University of Michigan\u2013Dearborn"],"raw_orcid":"https://orcid.org/0009-0001-1294-2375","affiliations":[{"raw_affiliation_string":"University of Michigan\u2013Dearborn","institution_ids":["https://openalex.org/I4210130704"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5120662096"],"corresponding_institution_ids":["https://openalex.org/I4210130704"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9911999702453613,"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":0.9911999702453613,"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":0.004800000227987766,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11614","display_name":"Cloud Data Security Solutions","score":0.0020000000949949026,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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-unlearning","display_name":"machine unlearning","score":0.9789999723434448},{"id":"https://openalex.org/keywords/data-deletion","display_name":"data deletion","score":0.5709999799728394},{"id":"https://openalex.org/keywords/linear-coding","display_name":"linear coding","score":0.5239999890327454},{"id":"https://openalex.org/keywords/data-sharding","display_name":"data sharding","score":0.7540000081062317},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.6549999713897705},{"id":"https://openalex.org/keywords/weak-learners","display_name":"weak learners","score":0.6819999814033508},{"id":"https://openalex.org/keywords/retraining-costs","display_name":"retraining costs","score":0.5529999732971191},{"id":"https://openalex.org/keywords/privacy-preserving-machine-learning","display_name":"privacy-preserving machine learning","score":0.5690000057220459}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8019000291824341},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.6535999774932861},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6484000086784363},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.5860999822616577},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.534500002861023},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.48910000920295715},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.45399999618530273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4275999963283539},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.39070001244544983},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.37709999084472656},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3441999852657318},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C2778445095","wikidata":"https://www.wikidata.org/wiki/Q18354077","display_name":"Sample complexity","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2660999894142151}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.5281/zenodo.17784600","doi":"https://doi.org/10.5281/zenodo.17784600","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.17784600","pdf_url":null,"source":{"id":"https://openalex.org/S4404674083","display_name":"International Journal of Leading Research Publication.","issn_l":"2582-8010","issn":["2582-8010"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},{"id":"doi:10.5281/zenodo.17784601","doi":"https://doi.org/10.5281/zenodo.17784601","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.17784601","pdf_url":null,"source":{"id":"https://openalex.org/S4404674083","display_name":"International Journal of Leading Research Publication.","issn_l":"2582-8010","issn":["2582-8010"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},{"id":"doi:10.5281/zenodo.18071753","doi":"https://doi.org/10.5281/zenodo.18071753","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18071753","pdf_url":null,"source":{"id":"https://openalex.org/S4404674083","display_name":"International Journal of Leading Research Publication.","issn_l":"2582-8010","issn":["2582-8010"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.5281/zenodo.17784600","doi":"https://doi.org/10.5281/zenodo.17784600","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.17784600","pdf_url":null,"source":{"id":"https://openalex.org/S4404674083","display_name":"International Journal of Leading Research Publication.","issn_l":"2582-8010","issn":["2582-8010"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3128806248","https://openalex.org/W4416513796","https://openalex.org/W4306808566","https://openalex.org/W7223202095","https://openalex.org/W3135378441","https://openalex.org/W4377864326","https://openalex.org/W3193974325","https://openalex.org/W4388505026","https://openalex.org/W4414692030","https://openalex.org/W7154222152"],"abstract_inverted_index":{"There":[0],"are":[1,94,132],"some":[2,53],"applications":[3],"that":[4,22,77,83,177],"may":[5,51],"necessitate":[6],"removing":[7,34,82],"the":[8,14,56,137,152,161,171,180],"trace":[9],"of":[10],"a":[11,19,35,63,127,146],"sample":[12,36,64,84],"from":[13,37,80,85],"system,":[15],"such":[16,91],"as":[17],"when":[18,28],"user":[20],"requests":[21,93],"their":[23,86],"data":[24,30,104,154],"be":[25,70,106],"deleted":[26],"or":[27],"corrupted":[29],"is":[31,65,123,155],"discovered.":[32],"Simply":[33],"storage":[38],"does":[39],"not":[40],"necessarily":[41],"remove":[42],"its":[43],"entire":[44],"trace,":[45],"because":[46],"downstream":[47],"machine":[48],"learning":[49,148,162],"models":[50,76,131],"store":[52],"information":[54],"about":[55],"samples":[57],"used":[58,78,124],"to":[59,114,125,135,160],"train":[60],"them.":[61],"If":[62],"completely":[66,71],"unlearned,":[67],"it":[68,79,178],"can":[69,105],"unlearned.":[72],"We":[73],"retrain":[74],"all":[75],"scratch,":[81],"training":[87,103,153],"dataset.":[88],"When":[89],"multiple":[90],"unlearning":[92,96,173,182],"anticipated,":[95],"by":[97],"retraining":[98],"becomes":[99],"prohibitively":[100],"costly.":[101],"The":[102],"divided":[107],"into":[108,157],"smaller":[109],"disjoint":[110],"shards":[111,158],"and":[112,175],"assigned":[113],"non-communicating":[115],"weak":[116],"learners":[117],"using":[118,164],"ensemble":[119],"learning.":[120],"Each":[121],"shard":[122],"create":[126],"faulty":[128],"model.":[129,140],"These":[130],"then":[133],"combined":[134],"form":[136],"final":[138],"central":[139],"In":[141,167],"this":[142],"paper,":[143],"we":[144,169],"propose":[145],"coded":[147],"protocol":[149,174],"in":[150],"which":[151],"encoded":[156],"prior":[159],"phase":[163],"linear":[165],"encoders.":[166],"addition,":[168],"present":[170],"corresponding":[172],"demonstrate":[176],"meets":[179],"perfect":[181],"criterion.":[183]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2025-12-03T00:00:00"}
