{"id":"https://openalex.org/W4383221514","doi":"https://doi.org/10.1145/3579856.3582821","title":"LDL: A Defense for Label-Based Membership Inference Attacks","display_name":"LDL: A Defense for Label-Based Membership Inference Attacks","publication_year":2023,"publication_date":"2023-07-05","ids":{"openalex":"https://openalex.org/W4383221514","doi":"https://doi.org/10.1145/3579856.3582821"},"language":"en","primary_location":{"id":"doi:10.1145/3579856.3582821","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3579856.3582821","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3579856.3582821","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Asia Conference on Computer and Communications Security","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3579856.3582821","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023144708","display_name":"Arezoo Rajabi","orcid":"https://orcid.org/0000-0001-9050-0129"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Arezoo Rajabi","raw_affiliation_strings":["University of Washington, United States of America"],"raw_orcid":"https://orcid.org/0000-0001-9050-0129","affiliations":[{"raw_affiliation_string":"University of Washington, United States of America","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033433868","display_name":"Dinuka Sahabandu","orcid":"https://orcid.org/0000-0001-7776-7865"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dinuka Sahabandu","raw_affiliation_strings":["University of Washington, USA"],"raw_orcid":"https://orcid.org/0000-0001-7776-7865","affiliations":[{"raw_affiliation_string":"University of Washington, USA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018806127","display_name":"Luyao Niu","orcid":"https://orcid.org/0000-0001-8591-5522"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Luyao Niu","raw_affiliation_strings":["University of Washington, United States of America"],"raw_orcid":"https://orcid.org/0000-0001-8591-5522","affiliations":[{"raw_affiliation_string":"University of Washington, United States of America","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052064870","display_name":"Bhaskar Ramasubramanian","orcid":"https://orcid.org/0000-0002-2166-7838"},"institutions":[{"id":"https://openalex.org/I52669646","display_name":"Western Washington University","ror":"https://ror.org/05wn7r715","country_code":"US","type":"education","lineage":["https://openalex.org/I52669646"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bhaskar Ramasubramanian","raw_affiliation_strings":["Western Washington University, United States of America"],"raw_orcid":"https://orcid.org/0000-0002-2166-7838","affiliations":[{"raw_affiliation_string":"Western Washington University, United States of America","institution_ids":["https://openalex.org/I52669646"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079723268","display_name":"Radha Poovendran","orcid":"https://orcid.org/0000-0003-0269-8097"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Radha Poovendran","raw_affiliation_strings":["Department of Electrical Engineering, University of Washington, United States of America"],"raw_orcid":"https://orcid.org/0000-0003-0269-8097","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Washington, United States of America","institution_ids":["https://openalex.org/I201448701"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5023144708"],"corresponding_institution_ids":["https://openalex.org/I201448701"],"apc_list":null,"apc_paid":null,"fwci":0.3408,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64425141,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"95","last_page":"108"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","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/T11689","display_name":"Adversarial Robustness in Machine Learning","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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9980999827384949,"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":0.9509999752044678,"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/overfitting","display_name":"Overfitting","score":0.9131429195404053},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7258369326591492},{"id":"https://openalex.org/keywords/adversary","display_name":"Adversary","score":0.7192804217338562},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6809254884719849},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6591135859489441},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6304800510406494},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5972984433174133},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5662850737571716},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4990832805633545},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4342227876186371},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3251439929008484},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.1208469569683075}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9131429195404053},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7258369326591492},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.7192804217338562},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6809254884719849},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6591135859489441},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6304800510406494},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5972984433174133},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5662850737571716},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4990832805633545},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4342227876186371},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3251439929008484},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.1208469569683075},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3579856.3582821","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3579856.3582821","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3579856.3582821","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Asia Conference on Computer and Communications Security","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3579856.3582821","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3579856.3582821","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3579856.3582821","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Asia Conference on Computer and Communications Security","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5}],"awards":[{"id":"https://openalex.org/G2572970162","display_name":null,"funder_award_id":"CNS-2153136","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6177982274","display_name":null,"funder_award_id":"N00014-20-1-2636","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G6934291839","display_name":null,"funder_award_id":"FA9550-20-1-0074","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"},{"id":"https://openalex.org/G7460104660","display_name":null,"funder_award_id":"2153136","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8876996369","display_name":null,"funder_award_id":"N00014","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309650","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"},{"id":"https://openalex.org/F4320338279","display_name":"Air Force Office of Scientific Research","ror":"https://ror.org/011e9bt93"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4383221514.pdf","grobid_xml":"https://content.openalex.org/works/W4383221514.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W1503398984","https://openalex.org/W2119874464","https://openalex.org/W2187089797","https://openalex.org/W2473418344","https://openalex.org/W2535690855","https://openalex.org/W2774510177","https://openalex.org/W2791319131","https://openalex.org/W2811973125","https://openalex.org/W2884943453","https://openalex.org/W2950943617","https://openalex.org/W2963564844","https://openalex.org/W3015161938","https://openalex.org/W3046518446","https://openalex.org/W3095315965","https://openalex.org/W3112787034","https://openalex.org/W3135347465","https://openalex.org/W4226136925","https://openalex.org/W4254372035","https://openalex.org/W4300449316","https://openalex.org/W6770149463"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W2786764570","https://openalex.org/W4309960894","https://openalex.org/W3042419602","https://openalex.org/W2966649771"],"abstract_inverted_index":{"The":[0],"data":[1],"used":[2,74,110],"to":[3,51,54,65,71,75,100,131],"train":[4,76],"deep":[5],"neural":[6],"network":[7],"(DNN)":[8],"models":[9,47,151],"in":[10],"applications":[11],"such":[12,57],"as":[13,58],"healthcare":[14],"and":[15,38,128],"finance":[16],"typically":[17,117],"contain":[18],"sensitive":[19],"information.":[20],"A":[21],"DNN":[22,150],"model":[23],"may":[24],"suffer":[25],"from":[26,121],"overfitting\u2013":[27],"it":[28],"will":[29],"perform":[30],"very":[31],"well":[32],"on":[33,40,149],"samples":[34,41,115],"seen":[35,43],"during":[36,44],"training,":[37],"poorly":[39],"not":[42,81],"training.":[45],"Overfitted":[46],"have":[48,101],"been":[49],"shown":[50,130],"be":[52,132,154],"susceptible":[53],"query-based":[55],"attacks":[56,61],"membership":[59],"inference":[60],"(MIAs).":[62],"MIAs":[63,89,109],"aim":[64],"determine":[66],"whether":[67],"a":[68,77,84,122,139,146],"sample":[69],"belongs":[70],"the":[72,111],"dataset":[73],"classifier":[78],"(members)":[79],"or":[80],"(nonmembers).":[82],"Recently,":[83],"new":[85],"class":[86],"of":[87,103,106],"label-based":[88],"(LAB":[90],"MIAs)":[91],"was":[92,97],"proposed,":[93],"where":[94],"an":[95,142,157],"adversary":[96,143],"only":[98],"required":[99],"knowledge":[102],"predicted":[104],"labels":[105],"samples.":[107],"LAB":[108,147],"insight":[112],"that":[113,152],"member":[114],"were":[116,129],"located":[118],"farther":[119],"away":[120],"classification":[123],"decision":[124],"boundary":[125],"than":[126],"nonmembers,":[127],"highly":[133],"effective":[134],"across":[135],"multiple":[136],"datasets.":[137],"Developing":[138],"defense":[140],"against":[141],"carrying":[144],"out":[145],"MIA":[148],"cannot":[153],"retrained":[155],"remains":[156],"open":[158],"problem.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}