{"id":"https://openalex.org/W4410543525","doi":"https://doi.org/10.3390/make7020043","title":"Membership Inference Attacks Fueled by Few-Shot Learning to Detect Privacy Leakage and Address Data Integrity","display_name":"Membership Inference Attacks Fueled by Few-Shot Learning to Detect Privacy Leakage and Address Data Integrity","publication_year":2025,"publication_date":"2025-05-20","ids":{"openalex":"https://openalex.org/W4410543525","doi":"https://doi.org/10.3390/make7020043"},"language":"en","primary_location":{"id":"doi:10.3390/make7020043","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020043","pdf_url":"https://www.mdpi.com/2504-4990/7/2/43/pdf?version=1747740485","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/7/2/43/pdf?version=1747740485","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110935327","display_name":"Daniel Jim\u00e9nez-L\u00f3pez","orcid":null},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]},{"id":"https://openalex.org/I4210095677","display_name":"Instituto Andaluz de Ciencias de la Tierra","ror":"https://ror.org/00v0g9w49","country_code":"ES","type":"facility","lineage":["https://openalex.org/I134820265","https://openalex.org/I4210095677"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Daniel Jim\u00e9nez-L\u00f3pez","raw_affiliation_strings":["Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain","institution_ids":["https://openalex.org/I4210095677","https://openalex.org/I173304897"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028526628","display_name":"Nuria Rodr\u00edguez-Barroso","orcid":"https://orcid.org/0000-0001-7172-9059"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]},{"id":"https://openalex.org/I4210095677","display_name":"Instituto Andaluz de Ciencias de la Tierra","ror":"https://ror.org/00v0g9w49","country_code":"ES","type":"facility","lineage":["https://openalex.org/I134820265","https://openalex.org/I4210095677"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Nuria Rodr\u00edguez-Barroso","raw_affiliation_strings":["Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain"],"raw_orcid":"https://orcid.org/0000-0001-7172-9059","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain","institution_ids":["https://openalex.org/I4210095677","https://openalex.org/I173304897"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015630332","display_name":"M. Victoria Luz\u00f3n","orcid":"https://orcid.org/0000-0002-7674-2137"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]},{"id":"https://openalex.org/I4210095677","display_name":"Instituto Andaluz de Ciencias de la Tierra","ror":"https://ror.org/00v0g9w49","country_code":"ES","type":"facility","lineage":["https://openalex.org/I134820265","https://openalex.org/I4210095677"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"M. Victoria Luz\u00f3n","raw_affiliation_strings":["Department of Software Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Software Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain","institution_ids":["https://openalex.org/I4210095677","https://openalex.org/I173304897"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017326471","display_name":"Javier Del Ser","orcid":"https://orcid.org/0000-0002-1260-9775"},"institutions":[{"id":"https://openalex.org/I169108374","display_name":"University of the Basque Country","ror":"https://ror.org/000xsnr85","country_code":"ES","type":"education","lineage":["https://openalex.org/I169108374"]},{"id":"https://openalex.org/I4210113430","display_name":"Tecnalia","ror":"https://ror.org/02fv8hj62","country_code":"ES","type":"other","lineage":["https://openalex.org/I4210113430"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Javier Del Ser","raw_affiliation_strings":["Department of Mathematics, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain","TECNALIA, Basque Research & Technology Alliance (BRTA), 20730 Azpeitia, Spain"],"raw_orcid":"https://orcid.org/0000-0002-1260-9775","affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain","institution_ids":["https://openalex.org/I169108374"]},{"raw_affiliation_string":"TECNALIA, Basque Research & Technology Alliance (BRTA), 20730 Azpeitia, Spain","institution_ids":["https://openalex.org/I4210113430"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045016749","display_name":"Francisco Herrera","orcid":"https://orcid.org/0000-0002-7283-312X"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]},{"id":"https://openalex.org/I4210095677","display_name":"Instituto Andaluz de Ciencias de la Tierra","ror":"https://ror.org/00v0g9w49","country_code":"ES","type":"facility","lineage":["https://openalex.org/I134820265","https://openalex.org/I4210095677"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Francisco Herrera","raw_affiliation_strings":["ADIA Lab, AI Maryah Island, Abu Dhabi P.O. Box 111999, United Arab Emirates","Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain"],"raw_orcid":"https://orcid.org/0000-0002-7283-312X","affiliations":[{"raw_affiliation_string":"ADIA Lab, AI Maryah Island, Abu Dhabi P.O. Box 111999, United Arab Emirates","institution_ids":[]},{"raw_affiliation_string":"Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain","institution_ids":["https://openalex.org/I4210095677","https://openalex.org/I173304897"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5028526628"],"corresponding_institution_ids":["https://openalex.org/I173304897","https://openalex.org/I4210095677"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04701372,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"7","issue":"2","first_page":"43","last_page":"43"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9958000183105469,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9517999887466431,"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/computer-security","display_name":"Computer security","score":0.6177975535392761},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5931934714317322},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5663008093833923},{"id":"https://openalex.org/keywords/leakage","display_name":"Leakage (economics)","score":0.5551676750183105},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.5399184226989746},{"id":"https://openalex.org/keywords/information-leakage","display_name":"Information leakage","score":0.5127562284469604},{"id":"https://openalex.org/keywords/internet-privacy","display_name":"Internet privacy","score":0.37350642681121826},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.21444857120513916},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.09468084573745728}],"concepts":[{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.6177975535392761},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5931934714317322},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5663008093833923},{"id":"https://openalex.org/C2777042071","wikidata":"https://www.wikidata.org/wiki/Q6509304","display_name":"Leakage (economics)","level":2,"score":0.5551676750183105},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.5399184226989746},{"id":"https://openalex.org/C2779201187","wikidata":"https://www.wikidata.org/wiki/Q2775060","display_name":"Information leakage","level":2,"score":0.5127562284469604},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.37350642681121826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21444857120513916},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.09468084573745728},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/make7020043","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020043","pdf_url":"https://www.mdpi.com/2504-4990/7/2/43/pdf?version=1747740485","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e2060896a7324642bbafe20c1a11b8cd","is_oa":true,"landing_page_url":"https://doaj.org/article/e2060896a7324642bbafe20c1a11b8cd","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction, Vol 7, Iss 2, p 43 (2025)","raw_type":"article"},{"id":"pmh:oai:dsp.tecnalia.com:11556/5948","is_oa":true,"landing_page_url":"https://hdl.handle.net/11556/5948","pdf_url":null,"source":{"id":"https://openalex.org/S4306402037","display_name":"TECNALIA Publications (Fundaci\u00f3n TECNALIA Research & Innovation)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210113430","host_organization_name":"Tecnalia","host_organization_lineage":["https://openalex.org/I4210113430"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"journal article"}],"best_oa_location":{"id":"doi:10.3390/make7020043","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020043","pdf_url":"https://www.mdpi.com/2504-4990/7/2/43/pdf?version=1747740485","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410543525.pdf","grobid_xml":"https://content.openalex.org/works/W4410543525.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W42722137","https://openalex.org/W2000159976","https://openalex.org/W2158698691","https://openalex.org/W2194775991","https://openalex.org/W2535690855","https://openalex.org/W2751484150","https://openalex.org/W2752373508","https://openalex.org/W2795435272","https://openalex.org/W2808498263","https://openalex.org/W2897830718","https://openalex.org/W2926319231","https://openalex.org/W2944378183","https://openalex.org/W2945237470","https://openalex.org/W2946948417","https://openalex.org/W2963078860","https://openalex.org/W2964137095","https://openalex.org/W3013068160","https://openalex.org/W3020547132","https://openalex.org/W3030163527","https://openalex.org/W3034312118","https://openalex.org/W3096692244","https://openalex.org/W3102998533","https://openalex.org/W3108878460","https://openalex.org/W3112689365","https://openalex.org/W3138815606","https://openalex.org/W3150395569","https://openalex.org/W3170237968","https://openalex.org/W4205505117","https://openalex.org/W4237413241","https://openalex.org/W4286567539","https://openalex.org/W4288057780","https://openalex.org/W4288346602","https://openalex.org/W4292779060","https://openalex.org/W4295806247","https://openalex.org/W4306931546","https://openalex.org/W4308410483","https://openalex.org/W4308643663","https://openalex.org/W4383860818","https://openalex.org/W4385187172","https://openalex.org/W4399270608","https://openalex.org/W4402915730","https://openalex.org/W6683971644","https://openalex.org/W6762744983","https://openalex.org/W6763192249"],"related_works":["https://openalex.org/W2074502265","https://openalex.org/W4214877189","https://openalex.org/W2773965352","https://openalex.org/W2381179799","https://openalex.org/W2980279061","https://openalex.org/W2334685461","https://openalex.org/W2366718574","https://openalex.org/W3123987581","https://openalex.org/W2358406440","https://openalex.org/W3175365978"],"abstract_inverted_index":{"Deep":[0],"learning":[1,61,116,163,215,226,261],"models":[2],"have":[3],"an":[4,179],"intrinsic":[5],"privacy":[6,18,68,87,128,158,201,221,256],"issue":[7],"as":[8,44,78,189],"they":[9],"memorize":[10],"parts":[11],"of":[12,47,85,95,114,156,160,170,185,208,213,223,246],"their":[13,70,76],"training":[14,210],"data,":[15],"creating":[16],"a":[17,45,59,102,123,144,161,224,241,259],"leakage.":[19],"Membership":[20],"inference":[21],"attacks":[22,65],"(MIAs)":[23],"exploit":[24],"this":[25,136,174],"to":[26,37,57,81,188,199,204,218,243],"obtain":[27],"confidential":[28],"information":[29],"about":[30],"the":[31,53,83,86,90,97,112,119,149,154,157,168,190,206,209,220,244,247],"data":[32,48,54,211],"used":[33,56],"for":[34,173],"training,":[35],"aiming":[36],"steal":[38],"information.":[39,266],"They":[40],"can":[41],"be":[42],"repurposed":[43],"measurement":[46],"integrity":[49,212],"by":[50,165],"inferring":[51],"whether":[52],"were":[55],"train":[58],"machine":[60],"model.":[62,227],"While":[63],"state-of-the-art":[64],"achieve":[66],"significant":[67],"leakage,":[69],"requirements":[71],"render":[72],"them":[73],"infeasible,":[74],"hindering":[75],"use":[77],"practical":[79],"tools":[80,198],"assess":[82,200],"magnitude":[84],"risk.":[88],"Moreover,":[89],"most":[91],"appropriate":[92],"evaluation":[93,129,155,207],"metric":[94],"MIA,":[96,147],"true":[98],"positive":[99,105],"rate":[100],"at":[101],"low":[103],"false":[104],"rate,":[106],"lacks":[107],"interpretability.":[108],"We":[109,142],"claim":[110],"that":[111,250],"incorporation":[113],"few-shot":[115,145],"techniques":[117],"into":[118],"MIA":[120,232],"field":[121],"and":[122,126,182,203,236,240],"suitable":[124],"qualitative":[125,183],"quantitative":[127,181],"measure":[130,184],"should":[131],"resolve":[132],"these":[133,194],"issues.":[134],"In":[135],"context,":[137],"our":[138,251],"proposal":[139],"is":[140],"twofold.":[141],"propose":[143,178],"learning-based":[146],"termed":[148],"FeS-MIA":[150],"model,":[151],"which":[152],"eases":[153],"breach":[159,222],"deep":[162,214,225,260],"model":[164,262],"significantly":[166],"reducing":[167],"number":[169],"resources":[171],"required":[172],"purpose.":[175],"Furthermore,":[176],"we":[177],"interpretable":[180],"privacy,":[186],"referred":[187],"Log-MIA":[191],"measure.":[192],"Jointly,":[193],"proposals":[195,252],"provide":[196],"new":[197],"leakages":[202,257],"ease":[205],"models,":[216],"i.e.,":[217],"analyze":[219],"Experiments":[228],"carried":[229],"out":[230],"with":[231,263],"over":[233],"image":[234],"classification":[235],"language":[237],"modeling":[238],"tasks,":[239],"comparison":[242],"state":[245],"art,":[248],"show":[249],"excel":[253],"in":[254,258],"identifying":[255],"little":[264],"extra":[265]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
