{"id":"https://openalex.org/W4283397204","doi":"https://doi.org/10.1109/eurosp53844.2022.00030","title":"Towards Fair and Robust Classification","display_name":"Towards Fair and Robust Classification","publication_year":2022,"publication_date":"2022-06-01","ids":{"openalex":"https://openalex.org/W4283397204","doi":"https://doi.org/10.1109/eurosp53844.2022.00030"},"language":"en","primary_location":{"id":"doi:10.1109/eurosp53844.2022.00030","doi":"https://doi.org/10.1109/eurosp53844.2022.00030","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eurosp53844.2022.00030","pdf_url":null,"source":{"id":"https://openalex.org/S4306418620","display_name":"IEEE European Symposium on Security and Privacy","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5035368302","display_name":"Haipei Sun","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haipei Sun","raw_affiliation_strings":["Facebook Inc., Seattle, WA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Facebook Inc., Seattle, WA","institution_ids":["https://openalex.org/I4210114444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101898735","display_name":"Kun Wu","orcid":"https://orcid.org/0009-0009-9954-0886","observed_orcids":["https://orcid.org/0009-0009-9954-0886"]},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kun Wu","raw_affiliation_strings":["Stevens Institute of Technology, Hoboken, NJ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology, Hoboken, NJ","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100428026","display_name":"Ting Wang","orcid":"https://orcid.org/0000-0003-4927-5833","observed_orcids":["https://orcid.org/0000-0003-4927-5833"]},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ting Wang","raw_affiliation_strings":["Penn State University, University Park, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Penn State University, University Park, PA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100460778","display_name":"Hui Wang","orcid":"https://orcid.org/0000-0002-3913-815X","observed_orcids":["https://orcid.org/0000-0002-3913-815X"]},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wendy Hui Wang","raw_affiliation_strings":["Stevens Institute of Technology, Hoboken, NJ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology, Hoboken, NJ","institution_ids":["https://openalex.org/I108468826"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3143,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.4979102,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"356","last_page":"376"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.7509999871253967,"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":0.7509999871253967,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.16599999368190765,"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":0.02459999918937683,"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/fair-classification","display_name":"fair classification","score":0.7289999723434448},{"id":"https://openalex.org/keywords/robust-classification","display_name":"robust classification","score":0.9629999995231628},{"id":"https://openalex.org/keywords/adversarial-robustness","display_name":"adversarial robustness","score":0.7879999876022339},{"id":"https://openalex.org/keywords/adversarial-training","display_name":"adversarial training","score":0.753000020980835},{"id":"https://openalex.org/keywords/input-disturbances","display_name":"input perturbation","score":0.6060000061988831},{"id":"https://openalex.org/keywords/surrogate-modeling","display_name":"surrogate models","score":0.7039999961853027}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8841037750244141},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8378744125366211},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.46653228998184204},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45930325984954834},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4567374885082245},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/eurosp53844.2022.00030","doi":"https://doi.org/10.1109/eurosp53844.2022.00030","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eurosp53844.2022.00030","pdf_url":null,"source":{"id":"https://openalex.org/S4306418620","display_name":"IEEE European Symposium on Security and Privacy","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7578999996185303,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G7664103396","display_name":"SaTC: CORE: Medium: Privacy for All: Ensuring Fair Privacy Protection in Machine Learning","funder_award_id":"2029038","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1473189865","https://openalex.org/W1673923490","https://openalex.org/W1945616565","https://openalex.org/W1961345416","https://openalex.org/W1984753492","https://openalex.org/W2014352947","https://openalex.org/W2051267297","https://openalex.org/W2097246321","https://openalex.org/W2099942492","https://openalex.org/W2100960835","https://openalex.org/W2107397716","https://openalex.org/W2112507308","https://openalex.org/W2116666691","https://openalex.org/W2116984840","https://openalex.org/W2123223828","https://openalex.org/W2144906988","https://openalex.org/W2157928966","https://openalex.org/W2269778407","https://openalex.org/W2294710185","https://openalex.org/W2410216425","https://openalex.org/W2530395818","https://openalex.org/W2535690855","https://openalex.org/W2552767274","https://openalex.org/W2597603852","https://openalex.org/W2626801932","https://openalex.org/W2640329709","https://openalex.org/W2732098159","https://openalex.org/W2764072425","https://openalex.org/W2765384636","https://openalex.org/W2781896769","https://openalex.org/W2785011159","https://openalex.org/W2793398195","https://openalex.org/W2796514099","https://openalex.org/W2897978524","https://openalex.org/W2905526464","https://openalex.org/W2913848079","https://openalex.org/W2934487731","https://openalex.org/W2940648411","https://openalex.org/W2947227164","https://openalex.org/W2957114115","https://openalex.org/W2962700793","https://openalex.org/W2962718684","https://openalex.org/W2963341057","https://openalex.org/W2963448658","https://openalex.org/W2963779314","https://openalex.org/W2963969878","https://openalex.org/W2969896603","https://openalex.org/W2989093880","https://openalex.org/W3035729345","https://openalex.org/W3080783482","https://openalex.org/W3093056059","https://openalex.org/W3093272433","https://openalex.org/W3094239814","https://openalex.org/W3135970545","https://openalex.org/W3163222347","https://openalex.org/W3202787215","https://openalex.org/W4252564528","https://openalex.org/W4289258088","https://openalex.org/W4319301723"],"related_works":["https://openalex.org/W3102559011","https://openalex.org/W3193458960","https://openalex.org/W4286632124","https://openalex.org/W4402716339","https://openalex.org/W3035729345","https://openalex.org/W4401024084","https://openalex.org/W2994725839","https://openalex.org/W6943734735","https://openalex.org/W6907913894","https://openalex.org/W3041176569"],"abstract_inverted_index":{"Robustness":[0],"and":[1,18,44,57,62,79,105,120,167,190,196],"fairness":[2,19,28,56,61,119,195],"are":[3],"two":[4,92],"equally":[5],"important":[6],"issues":[7],"for":[8,152],"machine":[9],"learning":[10],"systems.":[11],"Despite":[12],"the":[13,51,70,99,109,128,131,139,142,146,153,156,162,177,203],"active":[14],"research":[15],"on":[16,26,183],"robustness":[17,63,121,197],"of":[20,202],"ML":[21,134],"recently,":[22],"these":[23,88],"efforts":[24],"focus":[25],"either":[27],"or":[29],"robustness,":[30,78],"but":[31,144],"not":[32,66,145],"both.":[33],"To":[34,86],"bridge":[35],"this":[36,39],"gap,":[37],"in":[38],"paper,":[40],"we":[41,90],"design":[42,91],"Fair":[43],"Robust":[45],"Classification":[46],"(FRoC)":[47],"models":[48,53],"that":[49,97,107,171,187],"equip":[50],"classification":[52],"with":[54,111,198],"both":[55,60,118,165,188,194],"robustness.":[58],"Meeting":[59],"constraints":[64],"is":[65,125],"trivial":[67],"due":[68],"to":[69,116,127,141,164],"tension":[71],"between":[72,76],"them.":[73],"The":[74],"trade-off":[75],"fairness,":[77],"model":[80,103,110,143,170],"accuracy":[81,200],"also":[82],"introduces":[83],"additional":[84],"challenge.":[85],"address":[87,117],"challenges,":[89],"FRoC":[93],"methods,":[94],"namely":[95],"FRoC-PRE":[96,150,191],"modifies":[98,108],"input":[100],"data":[101,159,166],"before":[102],"training,":[104],"FRoC-IN":[106,124,189],"an":[112],"adversarial":[113],"objective":[114],"function":[115],"during":[122],"training.":[123],"suitable":[126],"settings":[129,154],"where":[130,155],"users":[132,157],"(e.g.,":[133,158],"service":[135],"providers)":[136],"only":[137],"have":[138,161,173],"access":[140,163],"original":[147],"data,":[148],"while":[149],"works":[151],"owners)":[160],"a":[168],"surrogate":[169],"may":[172],"similar":[174],"architecture":[175],"as":[176],"target":[178,204],"model.":[179,205],"Our":[180],"extensive":[181],"experiments":[182],"real-world":[184],"datasets":[185],"demonstrate":[186],"can":[192],"achieve":[193],"insignificant":[199],"loss":[201]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2022-06-25T00:00:00"}