{"id":"https://openalex.org/W4411337279","doi":"https://doi.org/10.1109/sp61157.2025.00058","title":"Benchmarking Attacks on Learning with Errors","display_name":"Benchmarking Attacks on Learning with Errors","publication_year":2025,"publication_date":"2025-05-12","ids":{"openalex":"https://openalex.org/W4411337279","doi":"https://doi.org/10.1109/sp61157.2025.00058"},"language":"en","primary_location":{"id":"doi:10.1109/sp61157.2025.00058","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sp61157.2025.00058","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Symposium on Security and Privacy (SP)","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/A5042329783","display_name":"Emily Wenger","orcid":"https://orcid.org/0009-0006-3346-8226"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Emily Wenger","raw_affiliation_strings":["Meta AI,USA"],"affiliations":[{"raw_affiliation_string":"Meta AI,USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009594573","display_name":"Eshika Saxena","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eshika Saxena","raw_affiliation_strings":["Meta AI,USA"],"affiliations":[{"raw_affiliation_string":"Meta AI,USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030036305","display_name":"Mohamed Malhou","orcid":"https://orcid.org/0009-0005-8412-4135"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohamed Malhou","raw_affiliation_strings":["Meta AI,USA"],"affiliations":[{"raw_affiliation_string":"Meta AI,USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115856984","display_name":"Ellie Thieu","orcid":null},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ellie Thieu","raw_affiliation_strings":["University of Wisconsin-Madison,USA"],"affiliations":[{"raw_affiliation_string":"University of Wisconsin-Madison,USA","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002850656","display_name":"Kristin Lauter","orcid":"https://orcid.org/0000-0002-1320-696X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kristin Lauter","raw_affiliation_strings":["Meta AI,USA"],"affiliations":[{"raw_affiliation_string":"Meta AI,USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5042329783"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.8414,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.91347302,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"279","last_page":"297"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.989799976348877,"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":0.989799976348877,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9664000272750854,"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/benchmarking","display_name":"Benchmarking","score":0.9424567222595215},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6894387602806091},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38826462626457214},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.34329700469970703},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.06521087884902954}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.9424567222595215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6894387602806091},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38826462626457214},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.34329700469970703},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.06521087884902954},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sp61157.2025.00058","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sp61157.2025.00058","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Symposium on Security and Privacy (SP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5899999737739563}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W70060353","https://openalex.org/W80562455","https://openalex.org/W1598269861","https://openalex.org/W1820018505","https://openalex.org/W1989510734","https://openalex.org/W2141040012","https://openalex.org/W2163035440","https://openalex.org/W2399848133","https://openalex.org/W2400700555","https://openalex.org/W2489545452","https://openalex.org/W2768174108","https://openalex.org/W2794634409","https://openalex.org/W2920734175","https://openalex.org/W2954442424","https://openalex.org/W3013288840","https://openalex.org/W3097076849","https://openalex.org/W3097572003","https://openalex.org/W3108120815","https://openalex.org/W4205163511","https://openalex.org/W4205350912","https://openalex.org/W4385654471","https://openalex.org/W4388858457","https://openalex.org/W4399426994","https://openalex.org/W4400224650","https://openalex.org/W4406309620"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4238897586","https://openalex.org/W435179959","https://openalex.org/W2619091065","https://openalex.org/W2059640416","https://openalex.org/W1490753184","https://openalex.org/W2284465472","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Lattice":[0,71],"cryptography":[1],"schemes":[2],"based":[3],"on":[4,29,41,45,115],"the":[5,69,79,108,137,148,158,174,252],"learning":[6],"with":[7,199],"errors":[8],"(LWE)":[9],"hardness":[10],"assumption":[11],"have":[12],"been":[13],"standardized":[14,80,116],"by":[15,23],"NIST":[16],"for":[17,25,111,118,173,188,212,222,235],"use":[18],"as":[19,84,134],"post-quantum":[20],"cryptosystems,":[21],"and":[22,87,91,94,120,144,147,160,166,168,201],"HomomorphicEncryption.org":[24],"performing":[26],"encrypted":[27],"computations":[28],"sensitive":[30],"data.":[31],"Thus,":[32],"understanding":[33,101],"their":[34],"concrete":[35,66,103,243],"security":[36,43],"is":[37,52],"critical.":[38],"Most":[39],"work":[40],"LWE":[42,81,104,112,127],"focuses":[44],"theoretical":[46,246],"estimates":[47],"of":[48,102],"attack":[49,57],"performance,":[50],"which":[51],"important":[53],"but":[54],"may":[55],"overlook":[56],"nuances":[58],"arising":[59],"in":[60,129,163,194,218],"real-world":[61],"implementations.":[62],"The":[63],"sole":[64],"existing":[65],"benchmarking":[67],"effort,":[68],"Darmstadt":[70],"Challenge,":[72],"does":[73],"not":[74,229],"include":[75],"benchmarks":[76,110],"relevant":[77],"to":[78,132,216,254],"parameter":[82],"choices-such":[83],"small":[85,88,119],"secret":[86,113],"error":[89],"distributions,":[90],"Ring-LWE":[92],"(RLWE)":[93],"Module-LWE":[95],"(MLWE)":[96],"variants.":[97],"To":[98],"improve":[99],"our":[100],"security,":[105],"we":[106,179,204,249],"provide":[107],"first":[109,175],"recovery":[114],"parameters,":[117,224],"low-weight":[121],"(sparse)":[122],"secrets.":[123],"We":[124,156,240],"evaluate":[125],"four":[126],"attacks":[128,139,162,172,227],"these":[130],"settings":[131],"serve":[133],"a":[135],"baseline:":[136],"Search-LWE":[138],"uSVP":[140,226],"[9],":[141],"SALSA":[142,159,200],"[51],":[143],"Cool&Cruel":[145,161],"[44],":[146],"Decision-LWE":[149,210],"attack:":[150],"Dual":[151],"Hybrid":[152],"Meet-in-the-Middle":[153],"(MitM)":[154],"[21].":[155],"extend":[157],"significant":[164],"ways,":[165],"implement":[167],"scale":[169],"up":[170,215],"MitM":[171,207],"time.":[176],"For":[177],"example,":[178],"recover":[180,230],"hamming":[181,213],"weight":[182],"9":[183],"-":[184,196],"11":[185],"binomial":[186],"secrets":[187,232],"KYBER":[189],"<tex":[190],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[191],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$(\\kappa=2)$</tex>":[192],"parameters":[193],"28":[195],"36":[197],"hours":[198],"Cool&Cruel,":[202],"while":[203,225],"find":[205],"that":[206],"can":[208],"solve":[209],"instances":[211],"weights":[214],"4":[217],"under":[219],"an":[220],"hour":[221],"Kyber":[223],"do":[228],"any":[231],"after":[233],"running":[234],"more":[236],"than":[237],"1100":[238],"hours.":[239],"also":[241],"compare":[242],"performance":[244],"against":[245],"estimates.":[247],"Finally,":[248],"open":[250],"source":[251],"code":[253],"enable":[255],"future":[256],"research.":[257]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
