{"id":"https://openalex.org/W2922358817","doi":"https://doi.org/10.23919/isita.2018.8664346","title":"A Succinct Model for Re-identification of Mobility Traces Based on Small Training Data","display_name":"A Succinct Model for Re-identification of Mobility Traces Based on Small Training Data","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2922358817","doi":"https://doi.org/10.23919/isita.2018.8664346","mag":"2922358817"},"language":"en","primary_location":{"id":"doi:10.23919/isita.2018.8664346","is_oa":false,"landing_page_url":"https://doi.org/10.23919/isita.2018.8664346","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Symposium on Information Theory and Its Applications (ISITA)","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/A5085794030","display_name":"Takao Murakami","orcid":"https://orcid.org/0000-0002-5110-1261"},"institutions":[{"id":"https://openalex.org/I73613424","display_name":"National Institute of Advanced Industrial Science and Technology","ror":"https://ror.org/01703db54","country_code":"JP","type":"government","lineage":["https://openalex.org/I73613424"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Takao Murakami","raw_affiliation_strings":["National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan","institution_ids":["https://openalex.org/I73613424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5085794030"],"corresponding_institution_ids":["https://openalex.org/I73613424"],"apc_list":null,"apc_paid":null,"fwci":0.7341,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.81419119,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"164","last_page":"168"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9860000014305115,"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/T11896","display_name":"Opportunistic and Delay-Tolerant Networks","score":0.9728999733924866,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.7778207659721375},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.740783154964447},{"id":"https://openalex.org/keywords/stochastic-matrix","display_name":"Stochastic matrix","score":0.559812068939209},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5414806008338928},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.5073472857475281},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.4882078468799591},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.46643590927124023},{"id":"https://openalex.org/keywords/maximum-entropy-markov-model","display_name":"Maximum-entropy Markov model","score":0.4402191936969757},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.43039679527282715},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.42943570017814636},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4278677701950073},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4258204400539398},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.40857425332069397},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3896723985671997},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3636032044887543},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35368606448173523},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3508952260017395},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1506081223487854},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.137542724609375}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7778207659721375},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.740783154964447},{"id":"https://openalex.org/C49555168","wikidata":"https://www.wikidata.org/wiki/Q176583","display_name":"Stochastic matrix","level":3,"score":0.559812068939209},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5414806008338928},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.5073472857475281},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.4882078468799591},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.46643590927124023},{"id":"https://openalex.org/C196956702","wikidata":"https://www.wikidata.org/wiki/Q6795829","display_name":"Maximum-entropy Markov model","level":5,"score":0.4402191936969757},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.43039679527282715},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.42943570017814636},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4278677701950073},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4258204400539398},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.40857425332069397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3896723985671997},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3636032044887543},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35368606448173523},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3508952260017395},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1506081223487854},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.137542724609375},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/isita.2018.8664346","is_oa":false,"landing_page_url":"https://doi.org/10.23919/isita.2018.8664346","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Symposium on Information Theory and Its Applications (ISITA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5400000214576721,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1246381107","https://openalex.org/W1536564267","https://openalex.org/W1601795611","https://openalex.org/W1663973292","https://openalex.org/W1973980096","https://openalex.org/W1974653074","https://openalex.org/W2023751717","https://openalex.org/W2054141820","https://openalex.org/W2061204737","https://openalex.org/W2110953678","https://openalex.org/W2120911939","https://openalex.org/W2140465920","https://openalex.org/W2146950091","https://openalex.org/W2181831161","https://openalex.org/W2320335964","https://openalex.org/W2478708596","https://openalex.org/W2553284111","https://openalex.org/W2583266863","https://openalex.org/W2765830915","https://openalex.org/W2884126656","https://openalex.org/W3101373155","https://openalex.org/W6732765934"],"related_works":["https://openalex.org/W2382132287","https://openalex.org/W2130519334","https://openalex.org/W2161328464","https://openalex.org/W1985664346","https://openalex.org/W3113307220","https://openalex.org/W2379333870","https://openalex.org/W1542903750","https://openalex.org/W3164302780","https://openalex.org/W2363310627","https://openalex.org/W2118728396"],"abstract_inverted_index":{"Re-identification":[0],"of":[1,18,39,47,65,104,144],"mobility":[2],"traces":[3,31,156],"based":[4,157],"on":[5,158],"the":[6,16,30,37,45,68,79,102,121,136,159,169,173,179,184,194],"Markov":[7,80,137,187],"chain":[8,81,138,188],"model":[9,27,82,116,131],"has":[10,91],"been":[11],"widely":[12],"studied":[13],"to":[14,67,77],"understand":[15],"risk":[17],"location":[19,207],"privacy.":[20],"It":[21],"is":[22,42,76,107,208],"well":[23],"known":[24],"that":[25,93,119,178,193],"this":[26,74,94,110],"can":[28,50],"re-identify":[29],"with":[32],"very":[33,52,108],"high":[34],"accuracy":[35],"when":[36,101,203],"amount":[38,46,103],"training":[40,48,105,213],"data":[41,49,106],"large.":[43],"However,":[44],"be":[51],"small":[53,63],"in":[54,73,147],"practice,":[55],"since":[56],"a":[57,62,97,114,132,142,199],"user":[58,211],"generally":[59],"discloses":[60],"only":[61,204],"number":[64],"locations":[66],"public.":[69],"A":[70],"state-of-the-art":[71,122],"method":[72,95,123,128,171,181,196],"scenario":[75],"train":[78],"(transition":[83],"matrices)":[84],"via":[85,150],"tensor":[86,185],"factorization.":[87,152],"The":[88],"previous":[89],"work":[90],"shown":[92],"outperforms":[96,120,183,198],"random":[98,200],"guess":[99,201],"even":[100,202],"small.In":[109],"paper,":[111],"we":[112],"propose":[113],"succinct":[115],"for":[117],"re-identification":[118],"explained":[124],"above.":[125],"Our":[126],"proposed":[127,170,180,195],"does":[129],"not":[130],"transition":[133],"pattern":[134],"(unlike":[135],"model)":[139],"but":[140],"models":[141],"probability":[143,165],"being":[145],"located":[146],"each":[148],"region":[149],"matrix":[151],"Then":[153],"it":[154],"re-identifies":[155],"JS":[160],"(Jensen-Shannon)":[161],"divergence":[162],"between":[163],"two":[164],"distributions.":[166],"We":[167,190],"evaluate":[168],"using":[172],"Gowalla":[174],"dataset,":[175],"and":[176],"demonstrate":[177,192],"significantly":[182,197],"factorization-based":[186],"model.":[189],"also":[191],"one":[205],"single":[206],"available":[209],"per":[210],"as":[212],"data.":[214]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}