{"id":"https://openalex.org/W4404037711","doi":"https://doi.org/10.1109/allerton63246.2024.10735319","title":"Differentially Private Sinkhorn Algorithm","display_name":"Differentially Private Sinkhorn Algorithm","publication_year":2024,"publication_date":"2024-09-24","ids":{"openalex":"https://openalex.org/W4404037711","doi":"https://doi.org/10.1109/allerton63246.2024.10735319"},"language":"en","primary_location":{"id":"doi:10.1109/allerton63246.2024.10735319","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton63246.2024.10735319","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 60th Annual Allerton Conference on Communication, Control, and Computing","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100365355","display_name":"Jiaqi Wang","orcid":"https://orcid.org/0000-0002-1708-3573"},"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":"Jiaqi Wang","raw_affiliation_strings":["Cornell University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell University","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071112095","display_name":"Ziv Goldfeld","orcid":"https://orcid.org/0000-0003-3406-3950"},"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":"Ziv Goldfeld","raw_affiliation_strings":["Cornell University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell University","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T10237","display_name":"Cryptography and Data Security","score":0.9580000042915344,"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/T10237","display_name":"Cryptography and Data Security","score":0.9580000042915344,"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/T12002","display_name":"Computability, Logic, AI Algorithms","score":0.9193000197410583,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.6650866866111755},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4701906442642212}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6650866866111755},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4701906442642212}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/allerton63246.2024.10735319","is_oa":false,"landing_page_url":"https://doi.org/10.1109/allerton63246.2024.10735319","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 60th Annual Allerton Conference on Communication, Control, and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W385466589","https://openalex.org/W1583837637","https://openalex.org/W1891714937","https://openalex.org/W1992926795","https://openalex.org/W2071048859","https://openalex.org/W2154682027","https://openalex.org/W2158131535","https://openalex.org/W2534420330","https://openalex.org/W2594311007","https://openalex.org/W2793477525","https://openalex.org/W2911978475","https://openalex.org/W2954497094","https://openalex.org/W2962997028","https://openalex.org/W2963134136","https://openalex.org/W2963433607","https://openalex.org/W2970137035","https://openalex.org/W2988047989","https://openalex.org/W3157939312","https://openalex.org/W3168875907","https://openalex.org/W3215267046","https://openalex.org/W4210960245","https://openalex.org/W4212774754","https://openalex.org/W4297789633","https://openalex.org/W4316652262","https://openalex.org/W4320029477","https://openalex.org/W4385187849","https://openalex.org/W6682774789","https://openalex.org/W6682962330","https://openalex.org/W6737976933","https://openalex.org/W6741832134","https://openalex.org/W6755187585","https://openalex.org/W6763424294","https://openalex.org/W6780794824","https://openalex.org/W6796176653","https://openalex.org/W6803119220","https://openalex.org/W6810306013"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109"],"abstract_inverted_index":{"Optimal":[0],"transport":[1],"(OT)":[2],"theory":[3],"play":[4],"a":[5,35,60,90,108],"central":[6],"role":[7],"in":[8,151],"the":[9,23,30,65,84,104,123,138,164],"design":[10],"and":[11,68,117,176],"analysis":[12],"of":[13,22,64,173],"various":[14],"machine":[15],"learning":[16],"algorithms.":[17],"As":[18,159],"such,":[19],"approximate":[20],"computation":[21,172],"OT":[24,174],"cost":[25,175],"between":[26,126],"large-scale":[27],"dataset":[28],"via":[29],"popular":[31],"Sinkhorn":[32,66,85,115,166],"algorithms":[33],"forms":[34],"basic":[36],"primitive.":[37],"However,":[38],"this":[39,56,144],"approach":[40],"may":[41],"lead":[42],"to":[43,103,129],"privacy":[44,77,120],"violations":[45],"when":[46],"dealing":[47],"with":[48,71],"datasets":[49],"that":[50,82],"contain":[51],"sensitive":[52],"information.":[53],"To":[54,81],"address":[55],"predicament,":[57],"we":[58,97,136,162],"propose":[59],"differentially":[61,169],"private":[62,114,155,170],"variant":[63],"algorithm":[67,86,94,116,167],"couple":[69],"it":[70],"formal":[72,152],"guarantees":[73,153],"by":[74,99,121],"deriving":[75],"its":[76,119,180],"utility":[78],"tradeoff":[79],"(PUT).":[80],"end,":[83],"is":[87],"treated":[88],"as":[89],"block":[91],"coordinate":[92],"descent":[93],"scheme,":[95],"which":[96],"privatize":[98],"injecting":[100],"Gaussian":[101],"noise":[102],"iterates.":[105],"We":[106],"establish":[107],"linear":[109],"convergence":[110],"rate":[111],"for":[112,154,168],"our":[113],"analyze":[118],"controlling":[122],"R\u00e9nyi":[124],"divergence":[125],"outputs":[127],"corresponding":[128],"neighboring":[130],"input":[131],"dataset.":[132],"Combining":[133],"these":[134],"results":[135],"obtain":[137],"desired":[139],"PUT.":[140,181],"In":[141],"doing":[142],"so,":[143],"work":[145],"also":[146],"closes":[147],"an":[148,160],"existing":[149],"gap":[150],"constrained":[156],"nonlinear":[157],"optimization.":[158],"application,":[161],"employ":[163],"noisy":[165],"(approximate)":[171],"derive":[177],"insights":[178],"from":[179]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}