{"id":"https://openalex.org/W3122848142","doi":"https://doi.org/10.1109/globecom42002.2020.9322508","title":"Privacy Preserving Facial Recognition Against Model Inversion Attacks","display_name":"Privacy Preserving Facial Recognition Against Model Inversion Attacks","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3122848142","doi":"https://doi.org/10.1109/globecom42002.2020.9322508","mag":"3122848142"},"language":"en","primary_location":{"id":"doi:10.1109/globecom42002.2020.9322508","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322508","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","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/A5081141790","display_name":"Pavana Prakash","orcid":"https://orcid.org/0000-0002-5752-5778"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pavana Prakash","raw_affiliation_strings":["University of Houston, Houston, TX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Houston, Houston, TX","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006252544","display_name":"Jiahao Ding","orcid":"https://orcid.org/0000-0002-2867-4133"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiahao Ding","raw_affiliation_strings":["University of Houston, Houston, TX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Houston, Houston, TX","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057819004","display_name":"Hongning Li","orcid":"https://orcid.org/0000-0002-0383-9165"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongning Li","raw_affiliation_strings":["School of Telecommunications Engineering, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Telecommunications Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019193172","display_name":"Sai Mounika Errapotu","orcid":"https://orcid.org/0000-0002-3759-3802"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sai Mounika Errapotu","raw_affiliation_strings":["University of Texas at El Paso, El Paso, TX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at El Paso, El Paso, TX","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065373445","display_name":"Qingqi Pei","orcid":"https://orcid.org/0000-0001-7601-5434"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingqi Pei","raw_affiliation_strings":["School of Telecommunications Engineering, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Telecommunications Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047722991","display_name":"Miao Pan","orcid":"https://orcid.org/0000-0003-2138-4413"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Miao Pan","raw_affiliation_strings":["University of Houston, Houston, TX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Houston, Houston, TX","institution_ids":["https://openalex.org/I44461941"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.3643,"has_fulltext":false,"cited_by_count":50,"citation_normalized_percentile":{"value":0.97233013,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":100},"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9993000030517578,"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":0.9993000030517578,"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/T11448","display_name":"Face recognition and analysis","score":0.9965000152587891,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9941999912261963,"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-science","display_name":"Computer science","score":0.860561192035675},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6941176056861877},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6119663715362549},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.5490133166313171},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5088134407997131},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.49104833602905273},{"id":"https://openalex.org/keywords/adversary","display_name":"Adversary","score":0.47721368074417114},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3766099214553833},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33350449800491333},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32776665687561035},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.2831152677536011}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.860561192035675},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6941176056861877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6119663715362549},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.5490133166313171},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5088134407997131},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.49104833602905273},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.47721368074417114},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3766099214553833},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33350449800491333},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32776665687561035},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2831152677536011}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom42002.2020.9322508","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322508","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G2059520079","display_name":null,"funder_award_id":"U1636209","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3222614376","display_name":null,"funder_award_id":"US CNS-1646607,CNS-1801925,CNS-2029569","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7407643660","display_name":null,"funder_award_id":"61902292","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8781327080","display_name":null,"funder_award_id":"2019ZDLGY13-07","funder_id":"https://openalex.org/F4320336035","funder_display_name":"Shanxi Provincial Key Research and Development Project"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320336035","display_name":"Shanxi Provincial Key Research and Development Project","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1473189865","https://openalex.org/W1846470281","https://openalex.org/W2051267297","https://openalex.org/W2087681821","https://openalex.org/W2096733369","https://openalex.org/W2115252128","https://openalex.org/W2161969291","https://openalex.org/W2185917628","https://openalex.org/W2294710185","https://openalex.org/W2347864333","https://openalex.org/W2473418344","https://openalex.org/W2513140567","https://openalex.org/W2535690855","https://openalex.org/W2895805829","https://openalex.org/W2902799610","https://openalex.org/W2964318098","https://openalex.org/W2995073985","https://openalex.org/W2998690112","https://openalex.org/W3007192423","https://openalex.org/W3099206234","https://openalex.org/W6628547770","https://openalex.org/W6677618333"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W4246396837","https://openalex.org/W2482350142","https://openalex.org/W3176240006","https://openalex.org/W4320018150","https://openalex.org/W2040808657","https://openalex.org/W2918664383","https://openalex.org/W4320855730","https://openalex.org/W106056076","https://openalex.org/W2135200719"],"abstract_inverted_index":{"Machine":[0,26],"learning":[1],"has":[2],"a":[3,20,29,57,69,98,108,155],"vast":[4],"outreach":[5],"in":[6,103,209],"principal":[7],"applications":[8],"and":[9,22,49,146,183,215],"uses":[10,77],"large":[11],"amount":[12],"of":[13,87,100,111,117,127,142,157,168,172,192,205],"data":[14,44],"to":[15,24,52,82,115],"train":[16],"the":[17,42,78,84,88,92,101,118,124,173,185,194,203,211],"models,":[18],"prompting":[19],"viable":[21],"easy":[23],"use":[25],"Learning":[27],"as":[28,64],"Service":[30],"(MLaaS).":[31],"This":[32],"flexible":[33],"paradigm":[34],"however,":[35],"could":[36,55],"have":[37],"immense":[38],"privacy":[39],"implications":[40],"since":[41],"training":[43,93,120,175,212],"often":[45],"contains":[46],"sensitive":[47],"features,":[48],"adversarial":[50,61,162],"access":[51,163],"such":[53,63,97],"models":[54],"pose":[56],"security":[58],"risk.":[59],"In":[60],"attacks":[62,160],"model":[65,158],"inversion":[66,159,186,217],"attack":[67],"on":[68],"system":[70,133],"used":[71],"for":[72],"face":[73],"recognition,":[74],"an":[75],"adversary":[76],"output":[79],"(target":[80],"label)":[81],"reconstruct":[83],"input":[85],"(image":[86],"target":[89,169],"individual":[90],"from":[91],"dataset).":[94],"To":[95],"avert":[96],"vulnerability":[99],"system,":[102],"this":[104,138],"paper,":[105],"we":[106,153,201],"develop":[107],"novel":[109],"approach":[110],"applying":[112,193],"perceptual":[113],"hash":[114],"parts":[116],"given":[119],"images":[121,145,167],"that":[122,164],"leverages":[123],"functional":[125],"mechanism":[126],"image":[128,181,213],"hashing.":[129],"The":[130],"facial":[131],"recognition":[132],"is":[134,150],"then":[135],"trained":[136],"over":[137,197],"newly":[139],"created":[140],"dataset":[141,176,214],"perceptually":[143],"hashed":[144,166],"high":[147],"classification":[148],"accuracy":[149],"observed.":[151],"Furthermore,":[152],"demonstrate":[154],"series":[156],"emulating":[161],"yield":[165],"individuals":[170],"instead":[171],"original":[174,180],"images;":[177],"thereby":[178],"preventing":[179],"reconstruction":[182],"counteracting":[184,216],"attack.":[187,218],"Through":[188],"rigorous":[189],"empirical":[190],"evaluations":[191],"proposed":[195,207],"formulation":[196],"real":[198],"world":[199],"dataset,":[200],"verify":[202],"effectiveness":[204],"our":[206],"framework":[208],"protecting":[210]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":45},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
