{"id":"https://openalex.org/W3110781976","doi":"https://doi.org/10.1145/3427228.3427282","title":"Towards Realistic Membership Inferences: The Case of Survey Data","display_name":"Towards Realistic Membership Inferences: The Case of Survey Data","publication_year":2020,"publication_date":"2020-12-07","ids":{"openalex":"https://openalex.org/W3110781976","doi":"https://doi.org/10.1145/3427228.3427282","mag":"3110781976"},"language":"en","primary_location":{"id":"doi:10.1145/3427228.3427282","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3427228.3427282","pdf_url":null,"source":{"id":"https://openalex.org/S4306417673","display_name":"Annual Computer Security Applications Conference","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Annual Computer Security Applications 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/A5025022077","display_name":"Luke A. Bauer","orcid":"https://orcid.org/0000-0002-5740-4386"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Luke A. Bauer","raw_affiliation_strings":["University of Florida, United States of America"],"affiliations":[{"raw_affiliation_string":"University of Florida, United States of America","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014689112","display_name":"Vincent Bindschaedler","orcid":"https://orcid.org/0000-0002-3066-7354"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vincent Bindschaedler","raw_affiliation_strings":["University of Florida, United States of America"],"affiliations":[{"raw_affiliation_string":"University of Florida, United States of America","institution_ids":["https://openalex.org/I33213144"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5025022077"],"corresponding_institution_ids":["https://openalex.org/I33213144"],"apc_list":null,"apc_paid":null,"fwci":0.2206,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.54758983,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"116","last_page":"128"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9987000226974487,"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":0.9987000226974487,"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9707000255584717,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7531175017356873},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6591787338256836},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.6181296110153198},{"id":"https://openalex.org/keywords/adversary","display_name":"Adversary","score":0.5988953709602356},{"id":"https://openalex.org/keywords/survey-data-collection","display_name":"Survey data collection","score":0.4754714071750641},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4517596662044525},{"id":"https://openalex.org/keywords/aggregate-data","display_name":"Aggregate data","score":0.41892650723457336},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3944045901298523},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29730576276779175},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.20600518584251404},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1529000699520111},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13654735684394836}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7531175017356873},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6591787338256836},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.6181296110153198},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.5988953709602356},{"id":"https://openalex.org/C198477413","wikidata":"https://www.wikidata.org/wiki/Q7647069","display_name":"Survey data collection","level":2,"score":0.4754714071750641},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4517596662044525},{"id":"https://openalex.org/C2778058735","wikidata":"https://www.wikidata.org/wiki/Q4692253","display_name":"Aggregate data","level":2,"score":0.41892650723457336},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3944045901298523},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29730576276779175},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.20600518584251404},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1529000699520111},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13654735684394836},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3427228.3427282","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3427228.3427282","pdf_url":null,"source":{"id":"https://openalex.org/S4306417673","display_name":"Annual Computer Security Applications Conference","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Annual Computer Security Applications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1873763122","https://openalex.org/W1933435501","https://openalex.org/W1965555277","https://openalex.org/W1982084696","https://openalex.org/W1986168976","https://openalex.org/W1992735538","https://openalex.org/W2002458933","https://openalex.org/W2025492815","https://openalex.org/W2027595342","https://openalex.org/W2040228409","https://openalex.org/W2076000582","https://openalex.org/W2078136197","https://openalex.org/W2079017812","https://openalex.org/W2081390392","https://openalex.org/W2095272373","https://openalex.org/W2099111195","https://openalex.org/W2109426455","https://openalex.org/W2112287470","https://openalex.org/W2124940696","https://openalex.org/W2130671266","https://openalex.org/W2141481372","https://openalex.org/W2152498467","https://openalex.org/W2164662623","https://openalex.org/W2167246752","https://openalex.org/W2203798560","https://openalex.org/W2294409705","https://openalex.org/W2294904676","https://openalex.org/W2322785760","https://openalex.org/W2324363467","https://openalex.org/W2532520288","https://openalex.org/W2535690855","https://openalex.org/W2795435272","https://openalex.org/W2804072243","https://openalex.org/W2884280357","https://openalex.org/W2884943453","https://openalex.org/W2886253048","https://openalex.org/W2887995258","https://openalex.org/W2930926105","https://openalex.org/W2947018541","https://openalex.org/W2964151798","https://openalex.org/W2967985550","https://openalex.org/W2970716886","https://openalex.org/W3002731020","https://openalex.org/W3028302264","https://openalex.org/W3096692244","https://openalex.org/W3103245149","https://openalex.org/W4205228770","https://openalex.org/W6657138077"],"related_works":["https://openalex.org/W2013343313","https://openalex.org/W591343008","https://openalex.org/W1539684430","https://openalex.org/W2371295515","https://openalex.org/W2121409501","https://openalex.org/W3202928283","https://openalex.org/W2463566895","https://openalex.org/W4294956381","https://openalex.org/W2137488388","https://openalex.org/W2117054901"],"abstract_inverted_index":{"We":[0,29],"consider":[1],"the":[2,13,27,35,50,65,86,110,114],"problem":[3],"of":[4,15,42,49,64,76],"membership":[5,31,67,99,132],"inference":[6,32,133],"attacks":[7,33,44],"on":[8],"aggregate":[9],"survey":[10,124],"data":[11,125],"through":[12],"use":[14],"several":[16],"real-world":[17],"datasets":[18],"and":[19,37],"a":[20,24,46,54,60,73,91,105],"published":[21],"study":[22],"as":[23],"model":[25],"for":[26],"survey.":[28,115],"apply":[30],"from":[34],"literature,":[36],"discover":[38],"that":[39,109,122,142],"methodological":[40],"assumptions":[41],"existing":[43],"produce":[45],"misleading":[47],"picture":[48,63],"risk.":[51],"When":[52],"using":[53],"more":[55,61],"realistic":[56],"methodology,":[57],"experiments":[58],"reveal":[59],"nuanced":[62],"risk:":[66],"inferences":[68,100,145],"do":[69,119],"succeed,":[70],"but":[71,137],"only":[72,101],"small":[74],"subset":[75],"individuals":[77,136],"are":[78,146],"highly":[79,143],"vulnerable":[80],"to":[81,89,107,135,138],"them.":[82],"In":[83],"fact,":[84],"if":[85],"adversary":[87],"wishes":[88],"avoid":[90],"high":[92],"false":[93],"positive":[94],"rate,":[95],"she":[96,103],"should":[97],"perform":[98],"when":[102,130],"has":[104],"reason":[106],"believe":[108],"target":[111],"participated":[112],"in":[113],"However,":[116],"our":[117],"results":[118],"not":[120,134],"imply":[121],"publishing":[123],"is":[126],"inherently":[127],"safe.":[128],"Indeed,":[129],"applying":[131],"hospitals,":[139],"we":[140],"find":[141],"accurate":[144],"possible.":[147]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
