{"id":"https://openalex.org/W3031618955","doi":"https://doi.org/10.1109/tifs.2020.2997183","title":"Secure Metric Learning via Differential Pairwise Privacy","display_name":"Secure Metric Learning via Differential Pairwise Privacy","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3031618955","doi":"https://doi.org/10.1109/tifs.2020.2997183","mag":"3031618955"},"language":"en","primary_location":{"id":"doi:10.1109/tifs.2020.2997183","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tifs.2020.2997183","pdf_url":null,"source":{"id":"https://openalex.org/S61310614","display_name":"IEEE Transactions on Information Forensics and Security","issn_l":"1556-6013","issn":["1556-6013","1556-6021"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Forensics and Security","raw_type":"journal-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/A5100336792","display_name":"Jing Li","orcid":"https://orcid.org/0000-0001-7584-1240"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Jing Li","raw_affiliation_strings":["Centre for Artificial Intelligence (CAI), University of Technology Sydney, Ultimo, Australia"],"raw_orcid":"https://orcid.org/0000-0001-7584-1240","affiliations":[{"raw_affiliation_string":"Centre for Artificial Intelligence (CAI), University of Technology Sydney, Ultimo, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020554727","display_name":"Yuangang Pan","orcid":"https://orcid.org/0000-0002-7950-4900"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yuangang Pan","raw_affiliation_strings":["Centre for Artificial Intelligence (CAI), University of Technology Sydney, Ultimo, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Artificial Intelligence (CAI), University of Technology Sydney, Ultimo, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053058502","display_name":"Yulei Sui","orcid":"https://orcid.org/0000-0002-9510-6574"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yulei Sui","raw_affiliation_strings":["Centre for Artificial Intelligence (CAI), University of Technology Sydney, Ultimo, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Artificial Intelligence (CAI), University of Technology Sydney, Ultimo, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":null,"display_name":"Ivor W. Tsang","orcid":"https://orcid.org/0000-0003-2211-8176"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ivor W. Tsang","raw_affiliation_strings":["Centre for Artificial Intelligence (CAI), University of Technology Sydney, Ultimo, Australia"],"raw_orcid":"https://orcid.org/0000-0003-2211-8176","affiliations":[{"raw_affiliation_string":"Centre for Artificial Intelligence (CAI), University of Technology Sydney, Ultimo, Australia","institution_ids":["https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100336792"],"corresponding_institution_ids":["https://openalex.org/I114017466"],"apc_list":null,"apc_paid":null,"fwci":0.4077,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.69011839,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"15","issue":null,"first_page":"3640","last_page":"3652"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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":1.0,"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/T11045","display_name":"Privacy, Security, and Data Protection","score":0.9024999737739563,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.9593059420585632},{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.8914592862129211},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7337164878845215},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6728770732879639},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5406010746955872},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5204075574874878},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4723029136657715},{"id":"https://openalex.org/keywords/pairwise-independence","display_name":"Pairwise independence","score":0.4512769281864166},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4174787998199463},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4092687666416168},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4060525894165039},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3629782795906067},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3322346806526184},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21610894799232483},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.07119861245155334},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.06943318247795105}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.9593059420585632},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.8914592862129211},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7337164878845215},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6728770732879639},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5406010746955872},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5204075574874878},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4723029136657715},{"id":"https://openalex.org/C114928208","wikidata":"https://www.wikidata.org/wiki/Q17104952","display_name":"Pairwise independence","level":5,"score":0.4512769281864166},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4174787998199463},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4092687666416168},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4060525894165039},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3629782795906067},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3322346806526184},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21610894799232483},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.07119861245155334},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.06943318247795105},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C89231458","wikidata":"https://www.wikidata.org/wiki/Q4723976","display_name":"Algebra of random variables","level":4,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C101796028","wikidata":"https://www.wikidata.org/wiki/Q535587","display_name":"Moment-generating function","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tifs.2020.2997183","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tifs.2020.2997183","pdf_url":null,"source":{"id":"https://openalex.org/S61310614","display_name":"IEEE Transactions on Information Forensics and Security","issn_l":"1556-6013","issn":["1556-6013","1556-6021"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Forensics and Security","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1888224328","display_name":null,"funder_award_id":"DP200101328","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G3263795131","display_name":null,"funder_award_id":"DP180100106","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G423915692","display_name":null,"funder_award_id":"DE170101081","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":90,"referenced_works":["https://openalex.org/W1532499126","https://openalex.org/W1582126688","https://openalex.org/W1823600381","https://openalex.org/W1873763122","https://openalex.org/W1898424075","https://openalex.org/W1985511977","https://openalex.org/W1992697866","https://openalex.org/W1992926795","https://openalex.org/W2013823004","https://openalex.org/W2019704260","https://openalex.org/W2027595342","https://openalex.org/W2039365410","https://openalex.org/W2054922243","https://openalex.org/W2076434944","https://openalex.org/W2091913942","https://openalex.org/W2096870293","https://openalex.org/W2100799972","https://openalex.org/W2101771965","https://openalex.org/W2106053110","https://openalex.org/W2109824782","https://openalex.org/W2110654099","https://openalex.org/W2112380340","https://openalex.org/W2116731705","https://openalex.org/W2117154949","https://openalex.org/W2119874464","https://openalex.org/W2121727866","https://openalex.org/W2125678004","https://openalex.org/W2135930857","https://openalex.org/W2150611147","https://openalex.org/W2155195660","https://openalex.org/W2157364932","https://openalex.org/W2160113380","https://openalex.org/W2169861334","https://openalex.org/W2182267394","https://openalex.org/W2184139426","https://openalex.org/W2401999504","https://openalex.org/W2406036028","https://openalex.org/W2473418344","https://openalex.org/W2555454054","https://openalex.org/W2555897561","https://openalex.org/W2578791853","https://openalex.org/W2588052735","https://openalex.org/W2595058628","https://openalex.org/W2605258322","https://openalex.org/W2624001596","https://openalex.org/W2742326713","https://openalex.org/W2766587611","https://openalex.org/W2785582092","https://openalex.org/W2801388832","https://openalex.org/W2803428411","https://openalex.org/W2804811213","https://openalex.org/W2809607392","https://openalex.org/W2950943617","https://openalex.org/W2962845515","https://openalex.org/W2963026686","https://openalex.org/W2963367389","https://openalex.org/W2963695762","https://openalex.org/W2963881987","https://openalex.org/W2964332492","https://openalex.org/W2964357505","https://openalex.org/W2965487497","https://openalex.org/W2976195905","https://openalex.org/W2976826043","https://openalex.org/W3120740533","https://openalex.org/W3123601494","https://openalex.org/W4205228770","https://openalex.org/W4288563649","https://openalex.org/W4299880301","https://openalex.org/W6631637103","https://openalex.org/W6634810422","https://openalex.org/W6638501681","https://openalex.org/W6639352576","https://openalex.org/W6675751002","https://openalex.org/W6676639149","https://openalex.org/W6676757673","https://openalex.org/W6677205618","https://openalex.org/W6677328822","https://openalex.org/W6677855611","https://openalex.org/W6677892458","https://openalex.org/W6682891771","https://openalex.org/W6685690991","https://openalex.org/W6686137708","https://openalex.org/W6730323794","https://openalex.org/W6733307674","https://openalex.org/W6742633112","https://openalex.org/W6743732567","https://openalex.org/W6751663542","https://openalex.org/W6751949754","https://openalex.org/W6763736615","https://openalex.org/W6766351000"],"related_works":["https://openalex.org/W2117280242","https://openalex.org/W2894446834","https://openalex.org/W2356627548","https://openalex.org/W2118324279","https://openalex.org/W4385492263","https://openalex.org/W2056664669","https://openalex.org/W4236747468","https://openalex.org/W2018558585","https://openalex.org/W2272369031","https://openalex.org/W2194570607"],"abstract_inverted_index":{"Distance":[0],"Metric":[1],"Learning":[2],"(DML)":[3],"has":[4],"drawn":[5],"much":[6,215],"attention":[7],"over":[8],"the":[9,26,61,83,118,123,129,135,162,169,178,186,189,203,212,226],"last":[10],"two":[11],"decades.":[12],"A":[13],"number":[14],"of":[15,28,33,85,147,171,188],"previous":[16],"works":[17],"have":[18],"shown":[19],"that":[20,143,202],"it":[21],"performs":[22],"well":[23],"in":[24,51,134],"measuring":[25],"similarities":[27],"individuals":[29],"given":[30,122],"a":[31,158,173,196],"set":[32,230],"correctly":[34],"labeled":[35],"pairwise":[36,45,65,79,108,124,154,207],"data":[37,46,208],"by":[38,116],"domain":[39],"experts.":[40],"These":[41],"important":[42],"and":[43,76,199],"precisely-labeled":[44],"are":[47],"often":[48],"highly":[49],"sensitive":[50],"real":[52],"world":[53],"(e.g.,":[54],"patients":[55],"similarity).":[56],"This":[57],"paper":[58],"studies,":[59],"for":[60,89,107],"first":[62],"time,":[63],"how":[64],"information":[66],"can":[67],"be":[68,105],"leaked":[69],"to":[70,100,152,167,184],"attackers":[71],"during":[72],"distance":[73,191],"metric":[74,91],"learning,":[75],"develops":[77],"differential":[78,87,95],"privacy":[80,96,209,227],"(DPP),":[81],"generalizing":[82],"definition":[84],"standard":[86],"privacy,":[88],"secure":[90],"learning.":[92],"Unlike":[93],"traditional":[94],"which":[97],"only":[98],"applies":[99],"independent":[101],"samples,":[102],"thus":[103],"cannot":[104],"used":[106],"data,":[109,125],"DPP":[110,139],"successfully":[111],"deals":[112],"with":[113],"this":[114],"problem":[115],"reformulating":[117],"worst":[119],"case.":[120],"Specifically,":[121],"we":[126],"reveal":[127],"all":[128,222],"involved":[130],"correlations":[131],"among":[132],"pairs":[133],"constructed":[136],"undirected":[137],"graph.":[138],"is":[140,150,165,182,229],"then":[141],"formalized":[142],"defines":[144],"what":[145],"kind":[146],"DML":[148,175],"algorithm":[149],"private":[151],"preserve":[153],"data.":[155],"After":[156],"that,":[157],"case":[159],"study":[160],"employing":[161],"contrastive":[163],"loss":[164],"exhibited":[166],"clarify":[168],"details":[170],"implementing":[172],"DPP-":[174],"algorithm.":[176],"Particularly,":[177],"sensitivity":[179],"reduction":[180],"technique":[181],"proposed":[183,204],"enhance":[185],"utility":[187],"output":[190,213],"metric.":[192],"Experiments":[193],"both":[194],"on":[195],"toy":[197],"dataset":[198],"benchmarks":[200],"demonstrate":[201],"scheme":[205],"achieves":[206],"without":[210],"compromising":[211],"performance":[214],"(Accuracy":[216],"declines":[217],"less":[218],"than":[219],"0.01":[220],"throughout":[221],"benchmark":[223],"datasets":[224],"when":[225],"budget":[228],"at":[231],"4).":[232]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}