{"id":"https://openalex.org/W4410227413","doi":"https://doi.org/10.1109/wcnc61545.2025.10978648","title":"Popular Content Prediction Through Adversarial Autoencoder Using Anonymised Data","display_name":"Popular Content Prediction Through Adversarial Autoencoder Using Anonymised Data","publication_year":2025,"publication_date":"2025-03-24","ids":{"openalex":"https://openalex.org/W4410227413","doi":"https://doi.org/10.1109/wcnc61545.2025.10978648"},"language":"en","primary_location":{"id":"doi:10.1109/wcnc61545.2025.10978648","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc61545.2025.10978648","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Wireless Communications and Networking Conference (WCNC)","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/A5073334956","display_name":"Dennys Leite Maia","orcid":"https://orcid.org/0000-0002-9536-2025"},"institutions":[{"id":"https://openalex.org/I76903346","display_name":"University of Coimbra","ror":"https://ror.org/04z8k9a98","country_code":"PT","type":"education","lineage":["https://openalex.org/I76903346"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Deborah Vieira de Alencar Maia","raw_affiliation_strings":["University of Coimbra,CISUC/LASI, DEI"],"affiliations":[{"raw_affiliation_string":"University of Coimbra,CISUC/LASI, DEI","institution_ids":["https://openalex.org/I76903346"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074435675","display_name":"Jo\u00e3o P. Vilela","orcid":"https://orcid.org/0000-0001-5805-1351"},"institutions":[{"id":"https://openalex.org/I4210163266","display_name":"Inesc P&D Brasil","ror":"https://ror.org/05yps4z32","country_code":"BR","type":"nonprofit","lineage":["https://openalex.org/I4210163266"]},{"id":"https://openalex.org/I4210166615","display_name":"INESC TEC","ror":"https://ror.org/05fa8ka61","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210125590","https://openalex.org/I4210166615"]}],"countries":["BR","PT"],"is_corresponding":false,"raw_author_name":"Joao P Vilela","raw_affiliation_strings":["CISUC, CRACS/INESC TEC"],"affiliations":[{"raw_affiliation_string":"CISUC, CRACS/INESC TEC","institution_ids":["https://openalex.org/I4210163266","https://openalex.org/I4210166615"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022354164","display_name":"Mar\u00edlia Curado","orcid":"https://orcid.org/0000-0001-6760-4675"},"institutions":[{"id":"https://openalex.org/I76903346","display_name":"University of Coimbra","ror":"https://ror.org/04z8k9a98","country_code":"PT","type":"education","lineage":["https://openalex.org/I76903346"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Marilia Curado","raw_affiliation_strings":["University of Coimbra,CISUC/LASI, DEI"],"affiliations":[{"raw_affiliation_string":"University of Coimbra,CISUC/LASI, DEI","institution_ids":["https://openalex.org/I76903346"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5073334956"],"corresponding_institution_ids":["https://openalex.org/I76903346"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10200724,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.972100019454956,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12357","display_name":"Digital Media Forensic Detection","score":0.972100019454956,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.9187999963760376,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9126999974250793,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/autoencoder","display_name":"Autoencoder","score":0.8629162311553955},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7841964364051819},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.685867428779602},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5559282898902893},{"id":"https://openalex.org/keywords/content","display_name":"Content (measure theory)","score":0.5149972438812256},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.34352603554725647},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.22929883003234863},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13264364004135132}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8629162311553955},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7841964364051819},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.685867428779602},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5559282898902893},{"id":"https://openalex.org/C2778152352","wikidata":"https://www.wikidata.org/wiki/Q5165061","display_name":"Content (measure theory)","level":2,"score":0.5149972438812256},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.34352603554725647},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.22929883003234863},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13264364004135132},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcnc61545.2025.10978648","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc61545.2025.10978648","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Wireless Communications and Networking Conference (WCNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2219888463","https://openalex.org/W2624989916","https://openalex.org/W2900094317","https://openalex.org/W2917748490","https://openalex.org/W2969549713","https://openalex.org/W2995022099","https://openalex.org/W3016097478","https://openalex.org/W3045049179","https://openalex.org/W3049535205","https://openalex.org/W3084847664","https://openalex.org/W3089488038","https://openalex.org/W3091870957","https://openalex.org/W3093673989","https://openalex.org/W3109695251","https://openalex.org/W3114953370","https://openalex.org/W3134977619","https://openalex.org/W3152949661","https://openalex.org/W3217539601","https://openalex.org/W4282917351","https://openalex.org/W4390480702","https://openalex.org/W6773958561","https://openalex.org/W6842392677"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W2502115930","https://openalex.org/W2803255133","https://openalex.org/W4297051394","https://openalex.org/W4220775285","https://openalex.org/W3204019825"],"abstract_inverted_index":{"The":[0],"increasing":[1],"number":[2],"of":[3,23,97,123],"connected":[4],"and":[5,32,44,68,100,117,125],"autonomous":[6],"vehicles":[7],"generates":[8],"an":[9,26],"even":[10,127],"greater":[11],"demand":[12],"for":[13,54],"efficient":[14],"content":[15,24,34,57,98,103],"delivery":[16],"in":[17,121],"vehicular":[18,59],"networks.":[19],"Estimating":[20],"the":[21,36,94],"popularity":[22,99],"is":[25],"important":[27],"task":[28],"to":[29,38,41,91],"proactively":[30],"cache":[31],"distribute":[33],"throughout":[35],"networks":[37,60],"add":[39],"value":[40],"users'":[42,77],"experiences":[43],"reduce":[45],"network":[46],"congestion.":[47],"This":[48,88],"paper":[49],"presents":[50],"a":[51,63],"novel":[52],"approach":[53,112],"predicting":[55],"popular":[56,102],"on":[58,62,76],"based":[61],"Federated":[64],"Learning-Adversarial":[65],"Autoencoder":[66],"model":[67,81],"anonymised":[69],"data.":[70,130],"Unlike":[71],"prior":[72],"works":[73],"that":[74,110],"relied":[75],"raw":[78],"features,":[79],"our":[80,111],"protects":[82],"user":[83,106],"privacy":[84],"through":[85],"data":[86],"anonymisation.":[87],"allows":[89],"us":[90],"learn":[92],"from":[93],"hidden":[95],"patterns":[96],"deliver":[101],"without":[104],"compromising":[105],"privacy.":[107],"Experiments":[108],"showed":[109],"exceeded":[113],"traditional":[114],"collaborative":[115],"filtering":[116],"deep":[118],"learning":[119],"methods":[120],"terms":[122],"accuracy":[124],"robustness,":[126],"with":[128],"sparse":[129]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
