{"id":"https://openalex.org/W4408894065","doi":"https://doi.org/10.1145/3676641.3716014","title":"Practical Federated Recommendation Model Learning Using ORAM with Controlled Privacy","display_name":"Practical Federated Recommendation Model Learning Using ORAM with Controlled Privacy","publication_year":2025,"publication_date":"2025-03-27","ids":{"openalex":"https://openalex.org/W4408894065","doi":"https://doi.org/10.1145/3676641.3716014"},"language":"en","primary_location":{"id":"doi:10.1145/3676641.3716014","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3676641.3716014","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3676641.3716014?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3676641.3716014?download=true","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jinyu Liu","orcid":"https://orcid.org/0009-0006-0069-4542"},"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":"Jinyu Liu","raw_affiliation_strings":["The Pennsylvania State University, University Park, PA, USA"],"raw_orcid":"https://orcid.org/0009-0006-0069-4542","affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008550066","display_name":"Wenjie Xiong","orcid":"https://orcid.org/0000-0002-7626-2651"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenjie Xiong","raw_affiliation_strings":["Virginia Tech, Blacksburg, VA, USA"],"raw_orcid":"https://orcid.org/0000-0002-7626-2651","affiliations":[{"raw_affiliation_string":"Virginia Tech, Blacksburg, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024329178","display_name":"G. Edward Suh","orcid":"https://orcid.org/0000-0001-6409-9888"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"G. Edward Suh","raw_affiliation_strings":["NVIDIA, Westford, MA, USA and Cornell University, Ithaca, NY, USA"],"raw_orcid":"https://orcid.org/0000-0001-6409-9888","affiliations":[{"raw_affiliation_string":"NVIDIA, Westford, MA, USA and Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035750684","display_name":"Kiwan Maeng","orcid":"https://orcid.org/0000-0002-0321-8406"},"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":"Kiwan Maeng","raw_affiliation_strings":["The Pennsylvania State University, University Park, PA, USA"],"raw_orcid":"https://orcid.org/0000-0002-0321-8406","affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"913","last_page":"932"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"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":0.9998999834060669,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9944999814033508,"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":0.8028981685638428},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.44212740659713745},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.4212246537208557},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.4182766079902649},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.37739503383636475},{"id":"https://openalex.org/keywords/internet-privacy","display_name":"Internet privacy","score":0.34275585412979126},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2656376361846924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8028981685638428},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.44212740659713745},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.4212246537208557},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.4182766079902649},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.37739503383636475},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.34275585412979126},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2656376361846924}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3676641.3716014","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3676641.3716014","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3676641.3716014?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2","raw_type":"proceedings-article"},{"id":"pmh:oai:vtechworks.lib.vt.edu:10919/125145","is_oa":false,"landing_page_url":"https://hdl.handle.net/10919/125145","pdf_url":null,"source":{"id":"https://openalex.org/S4306400248","display_name":"VTechWorks (Virginia Tech)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I859038795","host_organization_name":"Virginia Tech","host_organization_lineage":["https://openalex.org/I859038795"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"Article - Refereed"}],"best_oa_location":{"id":"doi:10.1145/3676641.3716014","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3676641.3716014","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3676641.3716014?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5874067710","display_name":"SaTC: CORE: Small: Utilizing Untrusted Machine Learning Services Safely via Privacy-Preserving User Data Embedding","funder_award_id":"2349610","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":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4408894065.pdf","grobid_xml":"https://content.openalex.org/works/W4408894065.grobid-xml"},"referenced_works_count":84,"referenced_works":["https://openalex.org/W398859631","https://openalex.org/W1548777816","https://openalex.org/W1988374166","https://openalex.org/W2005756274","https://openalex.org/W2014465787","https://openalex.org/W2033092546","https://openalex.org/W2054688000","https://openalex.org/W2067768229","https://openalex.org/W2081909523","https://openalex.org/W2117241830","https://openalex.org/W2170993700","https://openalex.org/W2219888463","https://openalex.org/W2238694998","https://openalex.org/W2264338659","https://openalex.org/W2269813923","https://openalex.org/W2473418344","https://openalex.org/W2475334473","https://openalex.org/W2510843581","https://openalex.org/W2512971201","https://openalex.org/W2513306639","https://openalex.org/W2605350416","https://openalex.org/W2605800822","https://openalex.org/W2613283858","https://openalex.org/W2723293840","https://openalex.org/W2747329762","https://openalex.org/W2783501794","https://openalex.org/W2788246224","https://openalex.org/W2794733520","https://openalex.org/W2795193970","https://openalex.org/W2893370267","https://openalex.org/W2903975607","https://openalex.org/W2907887012","https://openalex.org/W2911910392","https://openalex.org/W2914576582","https://openalex.org/W2947435413","https://openalex.org/W2962745591","https://openalex.org/W2963367478","https://openalex.org/W2964182926","https://openalex.org/W2970606380","https://openalex.org/W2972801466","https://openalex.org/W2984100107","https://openalex.org/W2985716091","https://openalex.org/W2998378988","https://openalex.org/W3006563497","https://openalex.org/W3016366555","https://openalex.org/W3021475380","https://openalex.org/W3047789363","https://openalex.org/W3088731039","https://openalex.org/W3093945404","https://openalex.org/W3153687269","https://openalex.org/W3154109599","https://openalex.org/W3154608090","https://openalex.org/W3155243801","https://openalex.org/W3158783012","https://openalex.org/W3159408427","https://openalex.org/W3193626058","https://openalex.org/W3207218827","https://openalex.org/W4205097557","https://openalex.org/W4205228770","https://openalex.org/W4206314719","https://openalex.org/W4220660812","https://openalex.org/W4229012946","https://openalex.org/W4230464498","https://openalex.org/W4234041355","https://openalex.org/W4240935049","https://openalex.org/W4245659846","https://openalex.org/W4280578647","https://openalex.org/W4280642915","https://openalex.org/W4288057728","https://openalex.org/W4293023291","https://openalex.org/W4296591827","https://openalex.org/W4296604473","https://openalex.org/W4308083738","https://openalex.org/W4318348341","https://openalex.org/W4360831767","https://openalex.org/W4367060154","https://openalex.org/W4376122599","https://openalex.org/W4380874757","https://openalex.org/W4385187849","https://openalex.org/W4385412128","https://openalex.org/W4385412495","https://openalex.org/W4385481862","https://openalex.org/W4402264437","https://openalex.org/W4409248526"],"related_works":["https://openalex.org/W2584827882","https://openalex.org/W3195097297","https://openalex.org/W4225340788","https://openalex.org/W3038106605","https://openalex.org/W2513267613","https://openalex.org/W3049084372","https://openalex.org/W2528109871","https://openalex.org/W2940702331","https://openalex.org/W2905822832","https://openalex.org/W2240244939"],"abstract_inverted_index":{"Training":[0],"high-quality":[1],"recommendation":[2,36],"models":[3,37],"requires":[4],"collecting":[5],"sensitive":[6],"user":[7,43],"data.":[8],"The":[9],"popular":[10],"privacy-enhancing":[11],"training":[12,35,132,158],"method,":[13],"federated":[14],"learning":[15],"(FL),":[16],"cannot":[17],"be":[18],"used":[19],"practically":[20],"due":[21],"to":[22,44,87,114,136],"these":[23],"models'":[24],"large":[25,55,98],"embedding":[26],"tables.":[27],"This":[28],"paper":[29],"introduces":[30],"FEDORA,":[31],"a":[32,50,84,101],"system":[33],"for":[34],"with":[38,92,106],"FL.":[39,121],"FEDORA":[40,71,110,122,148],"allows":[41],"each":[42],"only":[45],"download,":[46],"train,":[47],"and":[48,77,94,103,139,165],"upload":[49],"small":[51],"subset":[52],"of":[53,120],"the":[54,64,73,89,97,146,171],"tables":[56],"based":[57],"on":[58,161],"their":[59],"private":[60,129,155],"data,":[61],"while":[62,133,159],"hiding":[63],"access":[65],"pattern":[66],"using":[67,128,154],"oblivious":[68],"memory":[69,78],"(ORAM).":[70],"reduces":[72],"ORAM's":[74,90],"prohibitive":[75],"latency":[76,138,164],"overheads":[79],"by":[80,127,153],"(1)":[81],"introducing":[82],"\u03b5-FDP,":[83],"formal":[85],"way":[86],"balance":[88],"privacy":[91],"performance,":[93],"(2)":[95],"placing":[96],"ORAM":[99],"in":[100],"power-":[102],"cost-efficient":[104],"SSD":[105,142,167],"SSD-friendly":[107],"optimizations.":[108],"Additionally,":[109],"is":[111],"carefully":[112],"designed":[113],"support":[115],"(3)":[116],"modern":[117],"operation":[118],"modes":[119],"achieves":[123,149],"high":[124,150],"model":[125,151],"accuracy":[126,152],"features":[130,156],"during":[131,157],"achieving":[134],"up":[135],"24\u00d7":[137],"over":[140,145,170],"1000\u00d7":[141],"lifetime":[143,168],"improvement":[144,169],"baseline.":[147,172],"achieving,":[160],"average,":[162],"5\u00d7":[163],"158\u00d7":[166]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}