{"id":"https://openalex.org/W4288079548","doi":"https://doi.org/10.1145/3383455.3422567","title":"CryptoCredit","display_name":"CryptoCredit","publication_year":2020,"publication_date":"2020-10-15","ids":{"openalex":"https://openalex.org/W4288079548","doi":"https://doi.org/10.1145/3383455.3422567"},"language":"en","primary_location":{"id":"doi:10.1145/3383455.3422567","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3383455.3422567","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3383455.3422567","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 First ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3383455.3422567","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027085382","display_name":"Leo de Castro","orcid":"https://orcid.org/0009-0005-3190-6416"},"institutions":[{"id":"https://openalex.org/I4210110987","display_name":"IIT@MIT","ror":"https://ror.org/01wp8zh54","country_code":"US","type":"facility","lineage":["https://openalex.org/I30771326","https://openalex.org/I4210110987"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Leo de Castro","raw_affiliation_strings":["MIT CSAIL, Cambridge, Massachusetts"],"affiliations":[{"raw_affiliation_string":"MIT CSAIL, Cambridge, Massachusetts","institution_ids":["https://openalex.org/I4210110987"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100434472","display_name":"Jiahao Chen","orcid":"https://orcid.org/0000-0002-4357-6574"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiahao Chen","raw_affiliation_strings":["J.P.Morgan AI Research"],"affiliations":[{"raw_affiliation_string":"J.P.Morgan AI Research","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030114656","display_name":"Antigoni Polychroniadou","orcid":"https://orcid.org/0009-0003-0125-2971"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antigoni Polychroniadou","raw_affiliation_strings":["J.P.Morgan AI Research"],"affiliations":[{"raw_affiliation_string":"J.P.Morgan AI Research","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5027085382"],"corresponding_institution_ids":["https://openalex.org/I4210110987"],"apc_list":null,"apc_paid":null,"fwci":0.1371,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.61263239,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9991000294685364,"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":0.9991000294685364,"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/T10237","display_name":"Cryptography and Data Security","score":0.9925000071525574,"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":0.9890000224113464,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8037615418434143},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.6543213725090027},{"id":"https://openalex.org/keywords/regression-testing","display_name":"Regression testing","score":0.5602864623069763},{"id":"https://openalex.org/keywords/dilemma","display_name":"Dilemma","score":0.4767911434173584},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44473952054977417},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4426421523094177},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43941184878349304},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4369296133518219},{"id":"https://openalex.org/keywords/homomorphic-encryption","display_name":"Homomorphic encryption","score":0.42546093463897705},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4124048948287964},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3864721655845642},{"id":"https://openalex.org/keywords/encryption","display_name":"Encryption","score":0.33674436807632446},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.25296664237976074},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17612537741661072},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.13443639874458313},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09614589810371399},{"id":"https://openalex.org/keywords/software-development","display_name":"Software development","score":0.09171360731124878}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8037615418434143},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.6543213725090027},{"id":"https://openalex.org/C161821725","wikidata":"https://www.wikidata.org/wiki/Q917415","display_name":"Regression testing","level":5,"score":0.5602864623069763},{"id":"https://openalex.org/C2778496695","wikidata":"https://www.wikidata.org/wiki/Q254128","display_name":"Dilemma","level":2,"score":0.4767911434173584},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44473952054977417},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4426421523094177},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43941184878349304},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4369296133518219},{"id":"https://openalex.org/C158338273","wikidata":"https://www.wikidata.org/wiki/Q2154943","display_name":"Homomorphic encryption","level":3,"score":0.42546093463897705},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4124048948287964},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3864721655845642},{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.33674436807632446},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.25296664237976074},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17612537741661072},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.13443639874458313},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09614589810371399},{"id":"https://openalex.org/C529173508","wikidata":"https://www.wikidata.org/wiki/Q638608","display_name":"Software development","level":3,"score":0.09171360731124878},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C186846655","wikidata":"https://www.wikidata.org/wiki/Q3398377","display_name":"Software construction","level":4,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3383455.3422567","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3383455.3422567","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3383455.3422567","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 First ACM International Conference on AI in Finance","raw_type":"proceedings-article"},{"id":"pmh:oai:dspace.mit.edu:1721.1/146106","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/146106","pdf_url":null,"source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ACM|ACM International Conference on AI in Finance","raw_type":"http://purl.org/eprint/type/ConferencePaper"}],"best_oa_location":{"id":"doi:10.1145/3383455.3422567","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3383455.3422567","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3383455.3422567","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 First ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4288079548.pdf","grobid_xml":"https://content.openalex.org/works/W4288079548.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W150223756","https://openalex.org/W1819662813","https://openalex.org/W1973428528","https://openalex.org/W1975780287","https://openalex.org/W1992282993","https://openalex.org/W2014352947","https://openalex.org/W2016127919","https://openalex.org/W2031533839","https://openalex.org/W2043944888","https://openalex.org/W2061554433","https://openalex.org/W2085953209","https://openalex.org/W2156030242","https://openalex.org/W2177209050","https://openalex.org/W2340696859","https://openalex.org/W2550925836","https://openalex.org/W2584805976","https://openalex.org/W2599025709","https://openalex.org/W2601882761","https://openalex.org/W2897154134","https://openalex.org/W2945976633","https://openalex.org/W2947633774","https://openalex.org/W2964031043","https://openalex.org/W2964060211","https://openalex.org/W3125884317","https://openalex.org/W4288617781","https://openalex.org/W4386564360","https://openalex.org/W6638208828","https://openalex.org/W6735355083","https://openalex.org/W6765646913"],"related_works":["https://openalex.org/W2539930818","https://openalex.org/W2350209916","https://openalex.org/W4393118461","https://openalex.org/W4390664647","https://openalex.org/W3012147850","https://openalex.org/W4313300189","https://openalex.org/W2949835517","https://openalex.org/W2475524763","https://openalex.org/W2601739120","https://openalex.org/W4384302763"],"abstract_inverted_index":{"When":[0],"developing":[1],"models":[2,72],"for":[3,36,76],"regulated":[4],"decision":[5],"making,":[6],"sensitive":[7,39,83],"features":[8,30,84],"like":[9],"age,":[10],"race":[11],"and":[12,17,69,73,99],"gender":[13],"cannot":[14],"be":[15,19,34,44,93],"used":[16],"must":[18],"obscured":[20],"from":[21],"model":[22,63],"developers":[23,64],"to":[24,33,65,95,112],"prevent":[25],"bias.":[26],"However,":[27],"the":[28,47,82,86,102],"remaining":[29],"still":[31],"need":[32],"tested":[35],"correlation":[37],"with":[38,46],"features,":[40],"which":[41],"can":[42,92],"only":[43],"done":[45],"knowledge":[48],"of":[49],"those":[50],"features.":[51],"We":[52,88],"resolve":[53],"this":[54],"dilemma":[55],"using":[56,101],"a":[57],"fully":[58],"homomorphic":[59],"encryption":[60],"scheme,":[61],"allowing":[62],"train":[66],"linear":[67],"regression":[68,71,97],"logistic":[70],"test":[74],"them":[75],"possible":[77],"bias":[78],"without":[79],"ever":[80],"revealing":[81],"in":[85],"clear.":[87],"demonstrate":[89],"how":[90],"it":[91],"applied":[94],"leave-one-out":[96],"testing,":[98],"show":[100],"adult":[103],"income":[104],"data":[105],"set":[106],"that":[107],"our":[108],"method":[109],"is":[110],"practical":[111],"run.":[113]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2022-07-28T00:00:00"}
