{"id":"https://openalex.org/W4376626934","doi":"https://doi.org/10.56553/popets-2023-0070","title":"Towards Sentence Level Inference Attack Against Pre-trained Language Models","display_name":"Towards Sentence Level Inference Attack Against Pre-trained Language Models","publication_year":2023,"publication_date":"2023-05-15","ids":{"openalex":"https://openalex.org/W4376626934","doi":"https://doi.org/10.56553/popets-2023-0070"},"language":"en","primary_location":{"id":"doi:10.56553/popets-2023-0070","is_oa":true,"landing_page_url":"https://doi.org/10.56553/popets-2023-0070","pdf_url":"https://petsymposium.org/popets/2023/popets-2023-0070.pdf","source":{"id":"https://openalex.org/S4210183172","display_name":"Proceedings on Privacy Enhancing Technologies","issn_l":"2299-0984","issn":["2299-0984"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings on Privacy Enhancing Technologies","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://petsymposium.org/popets/2023/popets-2023-0070.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054023418","display_name":"Kang Gu","orcid":"https://orcid.org/0000-0001-8541-6205"},"institutions":[{"id":"https://openalex.org/I107672454","display_name":"Dartmouth College","ror":"https://ror.org/049s0rh22","country_code":"US","type":"education","lineage":["https://openalex.org/I107672454"]},{"id":"https://openalex.org/I4210166639","display_name":"Dartmouth Hospital","ror":"https://ror.org/02j3qj605","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I4210166639"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Kang Gu","raw_affiliation_strings":["Dartmouth College","Dartmouth College Hanover, New Hampshire, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dartmouth College","institution_ids":["https://openalex.org/I4210166639","https://openalex.org/I107672454"]},{"raw_affiliation_string":"Dartmouth College Hanover, New Hampshire, USA","institution_ids":["https://openalex.org/I107672454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003342694","display_name":"Ehsanul Kabir","orcid":"https://orcid.org/0000-0002-4885-4422"},"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":"Ehsanul Kabir","raw_affiliation_strings":["Penn State University","Penn State University University Park, Pennsylvania, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Penn State University","institution_ids":["https://openalex.org/I130769515"]},{"raw_affiliation_string":"Penn State University University Park, Pennsylvania, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091951921","display_name":"Neha Ramsurrun","orcid":null},"institutions":[{"id":"https://openalex.org/I107672454","display_name":"Dartmouth College","ror":"https://ror.org/049s0rh22","country_code":"US","type":"education","lineage":["https://openalex.org/I107672454"]},{"id":"https://openalex.org/I4210166639","display_name":"Dartmouth Hospital","ror":"https://ror.org/02j3qj605","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I4210166639"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Neha Ramsurrun","raw_affiliation_strings":["Dartmouth College","Dartmouth College Hanover, New Hampshire, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dartmouth College","institution_ids":["https://openalex.org/I4210166639","https://openalex.org/I107672454"]},{"raw_affiliation_string":"Dartmouth College Hanover, New Hampshire, USA","institution_ids":["https://openalex.org/I107672454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035399743","display_name":"Soroush Vosoughi","orcid":"https://orcid.org/0000-0002-2564-8909"},"institutions":[{"id":"https://openalex.org/I107672454","display_name":"Dartmouth College","ror":"https://ror.org/049s0rh22","country_code":"US","type":"education","lineage":["https://openalex.org/I107672454"]},{"id":"https://openalex.org/I4210166639","display_name":"Dartmouth Hospital","ror":"https://ror.org/02j3qj605","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I4210166639"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Soroush Vosoughi","raw_affiliation_strings":["Dartmouth College","Dartmouth College Hanover, New Hampshire, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dartmouth College","institution_ids":["https://openalex.org/I4210166639","https://openalex.org/I107672454"]},{"raw_affiliation_string":"Dartmouth College Hanover, New Hampshire, USA","institution_ids":["https://openalex.org/I107672454"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032020253","display_name":"Shagufta Mehnaz","orcid":"https://orcid.org/0000-0001-5850-4568"},"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":"Shagufta Mehnaz","raw_affiliation_strings":["Penn State University","Penn State University University Park, Pennsylvania, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Penn State University","institution_ids":["https://openalex.org/I130769515"]},{"raw_affiliation_string":"Penn State University University Park, Pennsylvania, USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.2208,"has_fulltext":true,"cited_by_count":14,"citation_normalized_percentile":{"value":0.89510064,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"2023","issue":"3","first_page":"62","last_page":"78"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9897000193595886,"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/T10028","display_name":"Topic Modeling","score":0.9897000193595886,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9573000073432922,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9420999884605408,"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":0.8374767303466797},{"id":"https://openalex.org/keywords/adversary","display_name":"Adversary","score":0.7096635103225708},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6598096489906311},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6326215267181396},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6262362003326416},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.6174653172492981},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5985246300697327},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5919337868690491},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5900075435638428},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5711453557014465},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4627414345741272},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.43917953968048096},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4323456287384033},{"id":"https://openalex.org/keywords/attack-model","display_name":"Attack model","score":0.42244553565979004},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.14094528555870056},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.11456754803657532}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8374767303466797},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.7096635103225708},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6598096489906311},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6326215267181396},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6262362003326416},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.6174653172492981},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5985246300697327},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5919337868690491},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5900075435638428},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5711453557014465},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4627414345741272},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.43917953968048096},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4323456287384033},{"id":"https://openalex.org/C65856478","wikidata":"https://www.wikidata.org/wiki/Q3991682","display_name":"Attack model","level":2,"score":0.42244553565979004},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.14094528555870056},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.11456754803657532},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.56553/popets-2023-0070","is_oa":true,"landing_page_url":"https://doi.org/10.56553/popets-2023-0070","pdf_url":"https://petsymposium.org/popets/2023/popets-2023-0070.pdf","source":{"id":"https://openalex.org/S4210183172","display_name":"Proceedings on Privacy Enhancing Technologies","issn_l":"2299-0984","issn":["2299-0984"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings on Privacy Enhancing Technologies","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.56553/popets-2023-0070","is_oa":true,"landing_page_url":"https://doi.org/10.56553/popets-2023-0070","pdf_url":"https://petsymposium.org/popets/2023/popets-2023-0070.pdf","source":{"id":"https://openalex.org/S4210183172","display_name":"Proceedings on Privacy Enhancing Technologies","issn_l":"2299-0984","issn":["2299-0984"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings on Privacy Enhancing Technologies","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7200000286102295,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320310419","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4376626934.pdf"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W1473189865","https://openalex.org/W1614298861","https://openalex.org/W1649611255","https://openalex.org/W1945076080","https://openalex.org/W2022885785","https://openalex.org/W2047828095","https://openalex.org/W2051267297","https://openalex.org/W2089940449","https://openalex.org/W2108325777","https://openalex.org/W2122210511","https://openalex.org/W2131744502","https://openalex.org/W2157331557","https://openalex.org/W2205981794","https://openalex.org/W2396881363","https://openalex.org/W2473418344","https://openalex.org/W2535690855","https://openalex.org/W2606092111","https://openalex.org/W2664267452","https://openalex.org/W2884280357","https://openalex.org/W2890220768","https://openalex.org/W2896457183","https://openalex.org/W2912023992","https://openalex.org/W2936473415","https://openalex.org/W2964110616","https://openalex.org/W2970597249","https://openalex.org/W3011574394","https://openalex.org/W3027379683","https://openalex.org/W3035616549","https://openalex.org/W3046764764","https://openalex.org/W3096738375","https://openalex.org/W3102516861","https://openalex.org/W3107001518","https://openalex.org/W3113998794","https://openalex.org/W3132573559","https://openalex.org/W3133280145","https://openalex.org/W3138516171","https://openalex.org/W3162938759","https://openalex.org/W3166890286","https://openalex.org/W3173769540","https://openalex.org/W3183960553","https://openalex.org/W3209235691","https://openalex.org/W4214669216","https://openalex.org/W4221141038","https://openalex.org/W4221166069","https://openalex.org/W4224920097","https://openalex.org/W4229820657","https://openalex.org/W4241781926","https://openalex.org/W4287200326","https://openalex.org/W4287545908","https://openalex.org/W4287553002","https://openalex.org/W4288089799","https://openalex.org/W4292779060","https://openalex.org/W4298201312","https://openalex.org/W4318959511","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W2383111961","https://openalex.org/W2365952365","https://openalex.org/W2352448290","https://openalex.org/W2380820513","https://openalex.org/W2913146933","https://openalex.org/W4387796593","https://openalex.org/W2604394466","https://openalex.org/W2952603690","https://openalex.org/W2941205169","https://openalex.org/W4328053081"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"pre-trained":[3,137],"language":[4,34,58],"models":[5,35],"(e.g.,":[6],"BERT":[7,48],"and":[8,24,170,224],"GPT)":[9],"have":[10,68],"shown":[11],"the":[12,57,107,113,118,143,157,179,195,201,225],"superior":[13],"capability":[14],"of":[15,56,84,142,159,194,197,227],"textual":[16],"representation":[17],"learning,":[18],"benefiting":[19],"from":[20,106,132],"their":[21],"large":[22],"architectures":[23],"massive":[25],"training":[26],"corpora.":[27],"The":[28,54],"industry":[29],"has":[30,45],"also":[31,60],"quickly":[32],"embraced":[33],"to":[36,49,156,174,192],"develop":[37],"various":[38],"downstream":[39],"NLP":[40],"applications.":[41],"For":[42],"example,":[43],"Google":[44],"already":[46],"used":[47],"improve":[50],"its":[51,176],"search":[52],"system.":[53],"utility":[55],"embeddings":[59,134],"brings":[61],"about":[62],"potential":[63],"privacy":[64],"risks.":[65],"Prior":[66],"works":[67],"revealed":[69],"that":[70,112,186],"an":[71],"adversary":[72,114],"can":[73,115,128,189],"either":[74],"identify":[75,190],"whether":[76],"a":[77,82,91,123,139,152],"keyword":[78],"exists":[79],"or":[80],"gather":[81],"set":[83],"possible":[85],"candidates":[86],"for":[87],"each":[88],"word":[89],"in":[90,167,210,230],"sentence":[92,160],"embedding.":[93],"However,":[94],"these":[95,222],"attacks":[96],"cannot":[97],"recover":[98],"coherent":[99],"sentences":[100,209,217],"which":[101,127],"leak":[102],"high-level":[103],"semantic":[104],"information":[105],"original":[108],"text.":[109],"To":[110],"demonstrate":[111],"go":[116],"beyond":[117],"word-level":[119,153],"attack,":[120,126],"we":[121],"present":[122],"novel":[124],"decoder-based":[125],"reconstruct":[129],"meaningful":[130],"text":[131],"private":[133],"after":[135],"being":[136],"on":[138],"public":[140],"dataset":[141],"same":[144],"domain.":[145],"This":[146],"attack":[147,154,166,188],"is":[148],"more":[149],"challenging":[150],"than":[151],"due":[155],"complexity":[158],"structures.":[161],"We":[162,220],"comprehensively":[163],"evaluate":[164],"our":[165,187,205,228],"two":[168],"domains":[169],"with":[171],"different":[172],"settings":[173],"show":[175,185],"superiority":[177],"over":[178],"baseline":[180,202],"attacks.":[181,203],"Quantitative":[182],"experimental":[183],"results":[184],"up":[191],"3.5X":[193],"number":[196],"keywords":[198],"identified":[199],"by":[200],"Although":[204],"method":[206,229],"reconstructs":[207],"high-quality":[208],"many":[211],"cases,":[212],"it":[213],"often":[214],"produces":[215],"lower-quality":[216],"as":[218],"well.":[219],"discuss":[221],"cases":[223],"limitations":[226],"detail":[231]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}